@article{PalmVietenSalberetal., author = {Palm, Christoph and Vieten, Andrea and Salber, Dagmar and Pietrzyk, Uwe}, title = {Evaluation of Registration Strategies for Multi-modality Images of Rat Brain Slices}, series = {Physics in Medicine and Biology}, volume = {54}, journal = {Physics in Medicine and Biology}, number = {10}, doi = {10.1088/0031-9155/54/10/021}, pages = {3269 -- 3289}, abstract = {In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.}, subject = {Histologie}, language = {en} } @article{AxerAmuntsGraesseletal., author = {Axer, Markus and Amunts, Katrin and Gr{\"a}ßel, David and Palm, Christoph and Dammers, J{\"u}rgen and Axer, Hubertus and Pietrzyk, Uwe and Zilles, Karl}, title = {Novel Approach to the Human Connectome}, series = {NeuroImage}, volume = {54}, journal = {NeuroImage}, number = {2}, doi = {10.1016/j.neuroimage.2010.08.075}, pages = {1091 -- 1101}, abstract = {Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.}, subject = {Bildgebendes Verfahren}, language = {en} } @article{SouzaJrMendelStrasseretal., author = {Souza Jr., Luis Antonio de and Mendel, Robert and Strasser, Sophia and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box}, series = {Computers in Biology and Medicine}, volume = {135}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2021.104578}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-20126}, pages = {1 -- 14}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.}, subject = {Deep Learning}, language = {en} } @article{BauerHamacherBroeeretal., author = {Bauer, Dagmar and Hamacher, Kurt and Br{\"o}er, Stefan and Pauleit, Dirk and Palm, Christoph and Zilles, Karl and Coenen, Heinz H. and Langen, Karl-Josef}, title = {Preferred stereoselective brain uptake of D-serine}, series = {Nuclear Medicine and Biology}, volume = {32}, journal = {Nuclear Medicine and Biology}, number = {8}, doi = {10.1016/j.nucmedbio.2005.07.004}, pages = {793 -- 797}, abstract = {Although it has long been presumed that d-amino acids are uncommon in mammalians, substantial amounts of free d-serine have been detected in the mammalian brain. d-Serine has been demonstrated to be an important modulator of glutamatergic neurotransmission and acts as an agonist at the strychnine-insensitive glycine site of N-methyl-d-aspartate receptors. The blood-to-brain transfer of d-serine is thought to be extremely low, and it is assumed that d-serine is generated by isomerization of l-serine in the brain. Stimulated by the observation of a preferred transport of the d-isomer of proline at the blood-brain barrier, we investigated the differential uptake of [3H]-d-serine and [3H]-l-serine in the rat brain 1 h after intravenous injection using quantitative autoradiography. Surprisingly, brain uptake of [3H]-d-serine was significantly higher than that of [3H]-l-serine, indicating a preferred transport of the d-enantiomer of serine at the blood-brain barrier. This finding indicates that exogenous d-serine may have a direct influence on glutamatergic neurotransmission and associated diseases.}, subject = {Aminos{\"a}uren}, language = {en} } @article{LehmannPalm, author = {Lehmann, Thomas M. and Palm, Christoph}, title = {Color Line Search for Illuminant Estimation in Real World Scenes}, series = {Journal of the Optical Society of America (JOSA) A}, volume = {18}, journal = {Journal of the Optical Society of America (JOSA) A}, number = {11}, doi = {10.1364/JOSAA.18.002679}, pages = {2679 -- 2691}, abstract = {The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [H. C. Lee, J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee's method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee's strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee's method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces.}, language = {en} } @article{Palm, author = {Palm, Christoph}, title = {Color Texture Classification by Integrative Co-Occurrence Matrices}, series = {Pattern Recognition}, volume = {37}, journal = {Pattern Recognition}, number = {5}, doi = {10.1016/j.patcog.2003.09.010}, pages = {965 -- 976}, abstract = {Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20\% and 32\% for the first and second baseline, respectively.}, language = {en} } @article{BrownConsortiumZhouetal., author = {Brown, Peter and Consortium, RELISH and Zhou, Yaoqi and Palm, Christoph}, title = {Large expert-curated database for benchmarking document similarity detection in biomedical literature search}, series = {Database}, volume = {2019}, journal = {Database}, publisher = {Oxford University Pres}, doi = {10.1093/database/baz085}, pages = {1 -- 66}, abstract = {Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76\% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.}, subject = {Information Retrieval}, language = {en} } @article{NeuschaeferRubeLehmannPalmetal., author = {Neuschaefer-Rube, C. and Lehmann, Thomas M. and Palm, Christoph and Bredno, J. and Klajman, S. and Spitzer, Klaus}, title = {3D-Visualisierung glottaler Abduktionsbewegungen}, series = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, volume = {2001/2002}, journal = {Aktuelle phoniatrisch-p{\"a}daudiologische Aspekte}, number = {9}, publisher = {Median}, isbn = {3-922766-76-5}, pages = {58 -- 61}, language = {de} } @article{PalmDehnhardtVietenetal., author = {Palm, Christoph and Dehnhardt, Markus and Vieten, Andrea and Pietrzyk, Uwe and Bauer, Andreas and Zilles, Karl}, title = {3D rat brain tumors}, series = {Naunyn-Schmiedebergs Archives of Pharmacology}, volume = {371}, journal = {Naunyn-Schmiedebergs Archives of Pharmacology}, number = {R103}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus}, series = {Gut}, volume = {69}, journal = {Gut}, number = {4}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2019-319460}, pages = {615 -- 616}, abstract = {Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9\% on 14 cases with neoplastic BE.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{PalmLehmann, author = {Palm, Christoph and Lehmann, Thomas M.}, title = {Classification of Color Textures by Gabor Filtering}, series = {Machine GRAPHICS \& VISION}, volume = {11}, journal = {Machine GRAPHICS \& VISION}, number = {2/3}, pages = {195 -- 219}, language = {en} } @article{ArribasAntonelliFrazzonietal., author = {Arribas, Julia and Antonelli, Giulio and Frazzoni, Leonardo and Fuccio, Lorenzo and Ebigbo, Alanna and van der Sommen, Fons and Ghatwary, Noha and Palm, Christoph and Coimbra, Miguel and Renna, Francesco and Bergman, Jacques J.G.H.M. and Sharma, Prateek and Messmann, Helmut and Hassan, Cesare and Dinis-Ribeiro, Mario J.}, title = {Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis}, series = {Gut}, volume = {70}, journal = {Gut}, number = {8}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2020-321922}, pages = {1458 -- 1468}, abstract = {Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (\%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90\% (CI 85\% to 94\%)/89\% (CI 85\% to 92\%)/87\% (CI 83\% to 91\%)/91\% (CI 87\% to 94\%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.}, language = {en} } @article{EbigboMendelRueckertetal., author = {Ebigbo, Alanna and Mendel, Robert and R{\"u}ckert, Tobias and Schuster, Laurin and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Mende, Matthias and Steinbr{\"u}ck, Ingo and Faiss, Siegbert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Deprez, Pierre and Oyama, Tsuneo and Takahashi, Akiko and Seewald, Stefan and Sharma, Prateek and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of Artificial Intelligence: A pilot Study}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {09}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-1311-8570}, pages = {878 -- 883}, abstract = {Background and aims: The accurate differentiation between T1a and T1b Barrett's cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.}, subject = {Maschinelles Lernen}, language = {en} } @article{SouzaJrPassosMendeletal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks}, series = {Computers in Biology and Medicine}, volume = {126}, journal = {Computers in Biology and Medicine}, number = {November}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2020.104029}, pages = {12}, abstract = {Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90\% of accuracy for the patch-based approach and 85\% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.}, subject = {Maschinelles Lernen}, language = {en} } @article{MendelRauberSouzaJretal., author = {Mendel, Robert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation}, series = {Computers in Biology and Medicine}, volume = {154}, journal = {Computers in Biology and Medicine}, number = {March}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2023.106585}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-57790}, pages = {13}, abstract = {Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student's prediction. The combined outputs of the students' prediction with the teachers' correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.}, language = {en} } @article{MaierDesernoHandelsetal., author = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, title = {IJCARS: BVM 2021 special issue}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {16}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer}, doi = {10.1007/s11548-021-02534-7}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-21666}, pages = {2067 -- 2068}, abstract = {The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization. During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system. In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm. Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, L{\"u}beck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years. At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee. Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context.}, subject = {Bildgebendes Verfahren}, language = {en} } @article{SchulzePalmKerschbaumetal., author = {Schulze, Elke and Palm, Christoph and Kerschbaum, Maximilian and Seidel, Roman and Lehmann, Lars and Koller, Michael and Pfingsten, Andrea}, title = {KI-gest{\"u}tzte Untersuchung in der nicht-operativen Versorgung symptomgebender Erkrankungen des Kniegelenks - ein multiprofessionelles Konzept (KINEESIO)}, series = {MSK - Muskuloskelettale Physiotherapie}, volume = {28}, journal = {MSK - Muskuloskelettale Physiotherapie}, number = {5}, publisher = {Thieme}, issn = {2701-6986}, doi = {10.1055/a-2402-9982}, pages = {312 -- 321}, abstract = {Beschwerdebilder am Kniegelenk aufgrund muskuloskelettaler degenerativer oder verletzungsbedingter Erkrankungen sind h{\"a}ufig, nehmen im Alter zu und sind mit der steigenden Inanspruchnahme {\"a}rztlicher und therapeutischer Behandlungsmaßnahmen verbunden. Einer erfolgreichen Therapie gehen oft notwendige zeit- und ressourcenaufwendige Untersuchungen zur Erkennung und Differenzierung der patient*innenspezifischen Problematik voraus. Im Zusammenhang mit der nicht-operativen Versorgung des Kniegelenks hat ein sektor{\"u}bergreifendes multiprofessionelles Forschungsteam ein Konzept entwickelt, um k{\"u}nstliche neuronale Netze so zu trainieren, dass sie bei der {\"a}rztlichen und physiotherapeutischen Untersuchung unterst{\"u}tzend Einsatz finden k{\"o}nnen. Denn gerade in der Erfassung und Auswertung umfassender Datenmengen liegen große Potenziale in der K{\"u}nstlichen Intelligenz (KI) im Gesundheitswesen. Das Projekt KINEESIO trainiert und testet KI-gest{\"u}tzte Screening- Tools zur Untersuchung von Patient*innen mit Kniegelenkerkrankungen. Diese unterst{\"u}tzen die Abl{\"a}ufe zwischen Leistungserbringern und Patient*innen, tragen zu einer verbesserten Differenzierung individueller Beschwerdebilder bei und dienen Entscheidungsprozessen f{\"u}r eine ad{\"a}quate Versorgung. Dadurch sollen Ressourcen im Gesundheitswesen geschont und eine qualitativ hochwertige Therapie ausreichend erm{\"o}glicht werden.}, language = {de} } @article{HartmannWeihererSchiltzetal., author = {Hartmann, Robin and Weiherer, Maximilian and Schiltz, Daniel and Seitz, Stephan and Lotter, Luisa and Anker, Alexandra and Palm, Christoph and Prantl, Lukas and Br{\´e}bant, Vanessa}, title = {A Novel Method of Outcome Assessment in Breast Reconstruction Surgery: Comparison of Autologous and Alloplastic Techniques Using Three-Dimensional Surface Imaging}, series = {Aesthetic Plastic Surgery}, volume = {44}, journal = {Aesthetic Plastic Surgery}, publisher = {Springer}, address = {Heidelberg}, doi = {10.1007/s00266-020-01749-4}, pages = {1980 -- 1987}, abstract = {Background Breast reconstruction is an important coping tool for patients undergoing a mastectomy. There are numerous surgical techniques in breast reconstruction surgery (BRS). Regardless of the technique used, creating a symmetric outcome is crucial for patients and plastic surgeons. Three-dimensional surface imaging enables surgeons and patients to assess the outcome's symmetry in BRS. To discriminate between autologous and alloplastic techniques, we analyzed both techniques using objective optical computerized symmetry analysis. Software was developed that enables clinicians to assess optical breast symmetry using three-dimensional surface imaging. Methods Twenty-seven patients who had undergone autologous (n = 12) or alloplastic (n = 15) BRS received three-dimensional surface imaging. Anthropomorphic data were collected digitally using semiautomatic measurements and automatic measurements. Automatic measurements were taken using the newly developed software. To quantify symmetry, a Symmetry Index is proposed. Results Statistical analysis revealed that there is no dif- ference in the outcome symmetry between the two groups (t test for independent samples; p = 0.48, two-tailed). Conclusion This study's findings provide a foundation for qualitative symmetry assessment in BRS using automatized digital anthropometry. In the present trial, no difference in the outcomes' optical symmetry was detected between autologous and alloplastic approaches.}, subject = {Mammoplastik}, language = {en} } @article{HartmannWeihererSchiltzetal., author = {Hartmann, Robin and Weiherer, Maximilian and Schiltz, Daniel and Baringer, Magnus and Noisser, Vivien and H{\"o}sl, Vanessa and Eigenberger, Andreas and Seitz, Stefan and Palm, Christoph and Prantl, Lukas and Br{\´e}bant, Vanessa}, title = {New aspects in digital breast assessment: further refinement of a method for automated digital anthropometry}, series = {Archives of Gynecology and Obstetrics}, volume = {303}, journal = {Archives of Gynecology and Obstetrics}, publisher = {Springer Nature}, address = {Heidelberg}, issn = {1432-0711}, doi = {10.1007/s00404-020-05862-2}, pages = {721 -- 728}, abstract = {Purpose: In this trial, we used a previously developed prototype software to assess aesthetic results after reconstructive surgery for congenital breast asymmetry using automated anthropometry. To prove the consensus between the manual and automatic digital measurements, we evaluated the software by comparing the manual and automatic measurements of 46 breasts. Methods: Twenty-three patients who underwent reconstructive surgery for congenital breast asymmetry at our institution were examined and underwent 3D surface imaging. Per patient, 14 manual and 14 computer-based anthropometric measurements were obtained according to a standardized protocol. Manual and automatic measurements, as well as the previously proposed Symmetry Index (SI), were compared. Results: The Wilcoxon signed-rank test revealed no significant differences in six of the seven measurements between the automatic and manual assessments. The SI showed robust agreement between the automatic and manual methods. Conclusion: The present trial validates our method for digital anthropometry. Despite the discrepancy in one measurement, all remaining measurements, including the SI, showed high agreement between the manual and automatic methods. The proposed data bring us one step closer to the long-term goal of establishing robust instruments to evaluate the results of breast surgery.}, language = {en} } @article{GrassmannMengelkampBrandletal., author = {Graßmann, Felix and Mengelkamp, Judith and Brandl, Caroline and Harsch, Sebastian and Zimmermann, Martina E. and Linkohr, Birgit and Peters, Annette and Heid, Iris M. and Palm, Christoph and Weber, Bernhard H. F.}, title = {A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography}, series = {Ophtalmology}, volume = {125}, journal = {Ophtalmology}, number = {9}, publisher = {Elsevier}, doi = {10.1016/j.ophtha.2018.02.037}, pages = {1410 -- 1420}, abstract = {Purpose Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. Participants. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. Methods. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. Main Outcome Measures. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. Results. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92\% (95\% confidence interval, 89\%-92\%) and an overall accuracy of 63.3\%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50\% and 63\%, respectively. Importantly, the algorithm detected 84.2\% of all fundus images with definite signs of early or late AMD. Overall, 94.3\% of healthy fundus images were classified correctly. Conclusions Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.}, subject = {Senile Makuladegeneration}, language = {en} } @article{HartmannWeihererNieberleetal., author = {Hartmann, Robin and Weiherer, Maximilian and Nieberle, Felix and Palm, Christoph and Br{\´e}bant, Vanessa and Prantl, Lukas and Lamby, Philipp and Reichert, Torsten E. and Taxis, J{\"u}rgen and Ettl, Tobias}, title = {Evaluating smartphone-based 3D imaging techniques for clinical application in oral and maxillofacial surgery: A comparative study with the vectra M5}, series = {Oral and Maxillofacial Surgery}, volume = {29}, journal = {Oral and Maxillofacial Surgery}, publisher = {Springer Nature}, doi = {10.1007/s10006-024-01322-2}, pages = {17}, abstract = {PURPOSE This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard. METHODS Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry. Smartphone-based SMs were superimposed onto Vectra-based SMs. Linear measurements and volumetric evaluations were performed to evaluate surface-to-surface deviation. To assess inter-observer reliability, all measurements were performed independently by a second observer. Statistical analyses included Bland-Altman analyses, the Wilcoxon signed-rank test for paired samples, and Intraclass correlation coefficients. RESULTS Photogrammetry-based SMs exhibited an overall landmark-to-landmark deviation of M = 0.8 mm (SD =  ± 0.58 mm, n = 450), while TrueDepth-based SMs displayed a deviation of M = 1.1 mm (SD =  ± 0.72 mm, n = 450). The mean volumetric difference for photogrammetry-based SMs was M = 1.8 cc (SD =  ± 2.12 cc, n = 90), and M = 3.1 cc (SD =  ± 2.64 cc, n = 90) for TrueDepth-based SMs. When comparing the two approaches, most landmark-to-landmark measurements demonstrated 95\% Bland-Altman limits of agreement (LoA) of ≤ 2 mm. Volumetric measurements revealed LoA > 2 cc. Photogrammetry-based measurements demonstrated higher inter-observer reliability for overall landmark-to-landmark deviation. CONCLUSION Both approaches for smartphone-based 3D surface imaging exhibit potential in capturing the face. Photogrammetry-based SMs demonstrated superior alignment and volumetric accuracy with Vectra-based SMs than TrueDepth-based SMs.}, language = {en} } @article{MaierPerretSimonetal., author = {Maier, Johannes and Perret, Jerome and Simon, Martina and Schmitt-R{\"u}th, Stephanie and Wittenberg, Thomas and Palm, Christoph}, title = {Force-feedback assisted and virtual fixtures based K-wire drilling simulation}, series = {Computers in Biology and Medicine}, volume = {114}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2019.103473}, pages = {1 -- 10}, abstract = {One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device. To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition. The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion. Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.}, subject = {Handchirurgie}, language = {en} } @article{RoemmeleMendelBarrettetal., author = {R{\"o}mmele, Christoph and Mendel, Robert and Barrett, Caroline and Kiesl, Hans and Rauber, David and R{\"u}ckert, Tobias and Kraus, Lisa and Heinkele, Jakob and Dhillon, Christine and Grosser, Bianca and Prinz, Friederike and Wanzl, Julia and Fleischmann, Carola and Nagl, Sandra and Schnoy, Elisabeth and Schlottmann, Jakob and Dellon, Evan S. and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, publisher = {Nature Portfolio}, address = {London}, doi = {10.1038/s41598-022-14605-z}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-46928}, pages = {10}, abstract = {The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.}, language = {en} } @article{RoserMeinikheimMuzalyovaetal., author = {Roser, David and Meinikheim, Michael and Muzalyova, Anna and Mendel, Robert and Palm, Christoph and Probst, Andreas and Nagl, Sandra and Scheppach, Markus W. and R{\"o}mmele, Christoph and Schnoy, Elisabeth and Parsa, Nasim and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial intelligence-assisted endoscopy and examiner confidence : a study on human-artificial intelligence interaction in Barrett's Esophagus (With Video)}, series = {DEN Open}, volume = {6}, journal = {DEN Open}, number = {1}, publisher = {Wiley}, doi = {10.1002/deo2.70150}, pages = {8}, abstract = {Objective Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI-assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non-experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16\% of all cases, and 9\% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non-experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI-assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice.}, language = {en} } @article{ScheppachRauberStallhoferetal., author = {Scheppach, Markus W. and Rauber, David and Stallhofer, Johannes and Muzalyova, Anna and Otten, Vera and Manzeneder, Carolin and Schwamberger, Tanja and Wanzl, Julia and Schlottmann, Jakob and Tadic, Vidan and Probst, Andreas and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Fleischmann, Carola and Meinikheim, Michael and Miller, Silvia and M{\"a}rkl, Bruno and Stallmach, Andreas and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm}, series = {Gastrointestinal Endoscopy}, journal = {Gastrointestinal Endoscopy}, publisher = {Elsevier}, doi = {10.1016/j.gie.2023.01.006}, abstract = {Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 \%, 76 \%, and 84 \% for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 \%, 72 \% and 67 \%, while the corresponding values in experts were 72 \%, 69 \% and 71 \%, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for "difficult" images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on "difficult" images suggests a further positive add-on effect in challenging cases.}, language = {en} } @article{ScheppachMendelMuzalyovaetal., author = {Scheppach, Markus W. and Mendel, Robert and Muzalyova, Anna and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Yip, Hon Chi and Lau, Louis Ho Shing and G{\"o}lder, Stefan Karl and Schmidt, Arthur and Kouladouros, Konstantinos and Abdelhafez, Mohamed and Walter, Benjamin M. and Meinikheim, Michael and Chiu, Philip Wai Yan and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial intelligence improves submucosal vessel detection during third space endoscopy}, series = {Endoscopy}, journal = {Endoscopy}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-2534-1164}, abstract = {Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4\% [CI 54.1-58.6] to 72.4\% [CI 70.3-74.4]. Endoscopists' VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5\% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions.}, language = {en} } @article{RueckertRueckertPalm, author = {R{\"u}ckert, Tobias and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art}, series = {Computers in Biology and Medicine}, volume = {169}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.compbiomed.2024.107929}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-69830}, pages = {24}, abstract = {In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.}, subject = {Deep Learning}, language = {en} } @article{RueweEigenbergerKleinetal., author = {Ruewe, Marc and Eigenberger, Andreas and Klein, Silvan and von Riedheim, Antonia and Gugg, Christine and Prantl, Lukas and Palm, Christoph and Weiherer, Maximilian and Zeman, Florian and Anker, Alexandra}, title = {Precise Monitoring of Returning Sensation in Digital Nerve Lesions by 3-D Imaging: A Proof-of-Concept Study}, series = {Plastic and Reconstructive Surgery}, volume = {152}, journal = {Plastic and Reconstructive Surgery}, number = {4}, publisher = {Lippincott Williams \& Wilkins}, address = {Philadelphia, Pa.}, organization = {American Society of Plastic Surgeons}, issn = {1529-4242}, doi = {10.1097/PRS.0000000000010456}, pages = {670e -- 674e}, abstract = {Digital nerve lesions result in a loss of tactile sensation reflected by an anesthetic area (AA) at the radial or ulnar aspect of the respective digit. Yet, available tools to monitor the recovery of tactile sense have been criticized for their lack of validity. However, the precise quantification of AA dynamics by three-dimensional (3-D) imaging could serve as an accurate surrogate to monitor recovery following digital nerve repair. For validation, AAs were marked on digits of healthy volunteers to simulate the AA of an impaired cutaneous innervation. Three dimensional models were composed from raw images that had been acquired with a 3-D camera (Vectra H2) to precisely quantify relative AA for each digit (3-D models, n= 80). Operator properties varied regarding individual experience in 3-D imaging and image processing. Additionally, the concept was applied in a clinical case study. Images taken by experienced photographers were rated better quality (p< 0.001) and needed less processing time (p= 0.020). Quantification of the relative AA was neither altered significantly by experience levels of the photographer (p= 0.425) nor the image assembler (p= 0.749). The proposed concept allows precise and reliable surface quantification of digits and can be performed consistently without relevant distortion by lack of examiner experience. Routine 3-D imaging of the AA has the great potential to provide visual evidence of various returning states of sensation and to convert sensory nerve recovery into a metric variable with high responsiveness to temporal progress.}, language = {en} } @article{HuberSchlosserStenzeletal., author = {Huber, Michaela and Schlosser, Daniela and Stenzel, Susanne and Maier, Johannes and Pattappa, Girish and Kujat, Richard and Striegl, Birgit and Docheva, Denitsa}, title = {Quantitative Analysis of Surface Contouring with Pulsed Bipolar Radiofrequency on Thin Chondromalacic Cartilage}, series = {BioMed Research International}, journal = {BioMed Research International}, publisher = {HINDAWI}, doi = {10.1155/2020/1242086}, pages = {1 -- 8}, abstract = {The purpose of this study was to evaluate the quality of surface contouring of chondromalacic cartilage by bipolar radio frequency energy using different treatment patterns in an animal model, as well as examining the impact of the treatment onto chondrocyte viability by two different methods. Our experiments were conducted on 36 fresh osteochondral sections from the tibia plateau of slaughtered 6-month-old pigs, where the thickness of the cartilage is similar to that of human wrist cartilage. An area of 1 cm(2) was first treated with emery paper to simulate the chondromalacic cartilage. Then, the treatment with RFE followed in 6 different patterns. The osteochondral sections were assessed for cellular viability (live/dead assay, caspase (cell apoptosis marker) staining, and quantitative analysed images obtained by fluorescent microscopy). For a quantitative characterization of none or treated cartilage surfaces, various roughness parameters were measured using confocal laser scanning microscopy (Olympus LEXT OLS 4000 3D). To describe the roughness, the Root-Mean-Square parameter (Sq) was calculated. A smoothing effect of the cartilage surface was detectable upon each pattern of RFE treatment. The Sq for native cartilage was Sq=3.8 +/- 1.1 mu m. The best smoothing pattern was seen for two RFE passes and a 2-second pulsed mode (B2p2) with an Sq=27.3 +/- 4.9 mu m. However, with increased smoothing, an augmentation in chondrocyte death up to 95\% was detected. Using bipolar RFE treatment in arthroscopy for small joints like the wrist or MCP joints should be used with caution. In the case of chondroplasty, there is a high chance to destroy the joint cartilage.}, language = {en} } @article{HuttererHattingenPalmetal., author = {Hutterer, Markus and Hattingen, Elke and Palm, Christoph and Proescholdt, Martin Andreas and Hau, Peter}, title = {Current standards and new concepts in MRI and PET response assessment of antiangiogenic therapies in high-grade glioma patients}, series = {Neuro-Oncology}, volume = {17}, journal = {Neuro-Oncology}, number = {6}, doi = {10.1093/neuonc/nou322}, pages = {784 -- 800}, abstract = {Despite multimodal treatment, the prognosis of high-grade gliomas is grim. As tumor growth is critically dependent on new blood vessel formation, antiangiogenic treatment approaches offer an innovative treatment strategy. Bevacizumab, a humanized monoclonal antibody, has been in the spotlight of antiangiogenic approaches for several years. Currently, MRI including contrast-enhanced T1-weighted and T2/fluid-attenuated inversion recovery (FLAIR) images is routinely used to evaluate antiangiogenic treatment response (Response Assessment in Neuro-Oncology criteria). However, by restoring the blood-brain barrier, bevacizumab may reduce T1 contrast enhancement and T2/FLAIR hyperintensity, thereby obscuring the imaging-based detection of progression. The aim of this review is to highlight the recent role of imaging biomarkers from MR and PET imaging on measurement of disease progression and treatment effectiveness in antiangiogenic therapies. Based on the reviewed studies, multimodal imaging combining standard MRI with new physiological MRI techniques and metabolic PET imaging, in particular amino acid tracers, may have the ability to detect antiangiogenic drug susceptibility or resistance prior to morphological changes. As advances occur in the development of therapies that target specific biochemical or molecular pathways and alter tumor physiology in potentially predictable ways, the validation of physiological and metabolic imaging biomarkers will become increasingly important in the near future.}, subject = {Gliom}, language = {en} } @article{EbigboPalmMessmann, author = {Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {Barrett esophagus: What to expect from Artificial Intelligence?}, series = {Best Practice \& Research Clinical Gastroenterology}, volume = {52-53}, journal = {Best Practice \& Research Clinical Gastroenterology}, number = {June-August}, publisher = {Elsevier}, issn = {1521-6918}, doi = {10.1016/j.bpg.2021.101726}, abstract = {The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.}, subject = {Deep Learning}, language = {en} } @article{MaierDesernoHandelsetal., author = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, title = {Guest editorial of the IJCARS - BVM 2018 special issue}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer}, doi = {10.1007/s11548-018-01902-0}, pages = {1 -- 2}, language = {en} } @article{PassosSouzaJrMendeletal., author = {Passos, Leandro A. and Souza Jr., Luis Antonio de and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{MaerklRueckertRauberetal., author = {Maerkl, Raphaela and Rueckert, Tobias and Rauber, David and Gutbrod, Max and Weber Nunes, Danilo and Palm, Christoph}, title = {Enhancing generalization in zero-shot multi-label endoscopic instrument classification}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {20}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer Nature}, doi = {10.1007/s11548-025-03439-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-85674}, pages = {1577 -- 1587}, abstract = {Purpose Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings. Methods This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies. Results The results demonstrate that combining sentence embeddings and z-score normalization significantly improves model performance. For unseen classes, the AUROC improves from 43.9\% to 64.9\%, and the multi-label accuracy from 26.1\% to 79.5\%. Overall performance measured across both seen and unseen classes improves from 49.3\% to 64.9\% in AUROC and from 37.3\% to 65.1\% in multi-label accuracy, highlighting the effectiveness of our approach. Conclusion These findings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.}, language = {en} } @article{WeihererEigenbergerEggeretal., author = {Weiherer, Maximilian and Eigenberger, Andreas and Egger, Bernhard and Br{\´e}bant, Vanessa and Prantl, Lukas and Palm, Christoph}, title = {Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans}, series = {The Visual Computer}, volume = {39}, journal = {The Visual Computer}, number = {4}, publisher = {Springer Nature}, doi = {10.1007/s00371-022-02431-3}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-30506}, pages = {1597 -- 1616}, abstract = {We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/.}, language = {en} } @article{BrebantWeihererNoisseretal., author = {Br{\´e}bant, Vanessa and Weiherer, Maximilian and Noisser, Vivien and Seitz, Stephan and Prantl, Lukas and Eigenberger, Andreas}, title = {Implants Versus Lipograft: Analysis of Long-Term Results Following Congenital Breast Asymmetry Correction}, series = {Aesthetic Plastic Surgery}, volume = {46}, journal = {Aesthetic Plastic Surgery}, publisher = {Springer Nature}, doi = {10.1007/s00266-022-02843-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-32404}, pages = {2228 -- 2236}, abstract = {Aims Congenital breast asymmetry represents a particular challenge to the classic techniques of plastic surgery given the young age of patients at presentation. This study reviews and compares the long-term results of traditional breast augmentation using silicone implants and the more innovative technique of lipografting. Methods To achieve this, we not only captured subjective parameters such as satisfaction with outcome and symmetry, but also objective parameters including breast vol-ume and anthropometric measurements. The objective examination was performed manually and by using the Vectra H2 photogrammetry scanning system. Results Differences between patients undergoing either implant augmentation or lipograft were revealed not to be significant with respect to patient satisfaction with surgical outcome (p= 0.55) and symmetry (p= 0.69). Furthermore, a breast symmetry of 93 \% was reported in both groups. Likewise, no statistically significant volume difference between the left and right breasts was observed in both groups (p\0.41). However, lipograft patients needed on average 2.9 procedures to achieve the desired result, compared with 1.3 for implant augmentation. In contrast, patients treated with implant augmentation may require anumber of implant changes during their lifetime. Conclusion Both methods may be considered for patients presenting with congenital breast asymmetry.}, language = {en} } @article{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Imitating human soft tissue on basis of a dual-material 3D print using a support-filled metamaterial to provide bimanual haptic for a hand surgery training system}, series = {Quantitative Imaging in Medicine and Surgery}, volume = {9}, journal = {Quantitative Imaging in Medicine and Surgery}, number = {1}, publisher = {AME Publishing Company}, doi = {10.21037/qims.2018.09.17}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-979}, pages = {30 -- 42}, abstract = {Background: Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D-printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand. Methods: The goal of this experiment is to imitate human soft tissue with its haptic and elasticity for a realistic hand phantom fabrication, using only a dual-material 3D printer and support-material-filled metamaterial between skin and bone. We present our workflow to generate lattice structures between hard bone and soft skin with iterative cube edge (CE) or cube face (CF) unit cells. Cuboid and finger shaped sample prints with and without inner hard bone in different lattice thickness are constructed and 3D printed. Results: The most elastic available rubber-like material is too firm to imitate soft tissue. By reducing the amount of rubber in the inner volume through support material (SUP), objects become significantly softer. Without metamaterial, after disintegration, the SUP can be shifted through the volume and thus the body loses its original shape. Although the CE design increases the elasticity, it cannot restore the fabric form. In contrast to CE, the CF design increases not only the elasticity but also guarantees a local limitation of the SUP. Therefore, the body retains its shape and internal bones remain in its intended place. Various unit cell sizes, lattice thickening and skin thickness regulate the rubber material and SUP ratio. Test prints with higher SUP and lower rubber material percentage appear softer and vice versa. This was confirmed by an expert surgeon evaluation. Subjects adjudged pure rubber-like material as too firm and samples only filled with SUP or lattice structure in CE design as not suitable for imitating tissue. 3D-printed finger samples in CF design were rated as realistic compared to the haptic of human tissue with a good palpable bone structure. Conclusions: We developed a new dual-material 3D print technique to imitate soft tissue of the human hand with its haptic properties. Blowy SUP is trapped within a lattice structure to soften rubber-like 3D print material, which makes it possible to reproduce a realistic replica of human hand soft tissue.}, subject = {Handchirurgie}, language = {en} } @article{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Optically tracked and 3D printed haptic phantom hand for surgical training system}, series = {Quantitative Imaging in Medicine and Surgery}, volume = {10}, journal = {Quantitative Imaging in Medicine and Surgery}, number = {02}, publisher = {AME Publishing Company}, address = {Hong Kong, China}, doi = {10.21037/qims.2019.12.03}, pages = {340 -- 455}, abstract = {Background: For surgical fixation of bone fractures of the human hand, so-called Kirschner-wires (K-wires) are drilled through bone fragments. Due to the minimally invasive drilling procedures without a view of risk structures like vessels and nerves, a thorough training of young surgeons is necessary. For the development of a virtual reality (VR) based training system, a three-dimensional (3D) printed phantom hand is required. To ensure an intuitive operation, this phantom hand has to be realistic in both, its position relative to the driller as well as in its haptic features. The softest 3D printing material available on the market, however, is too hard to imitate human soft tissue. Therefore, a support-material (SUP) filled metamaterial is used to soften the raw material. Realistic haptic features are important to palpate protrusions of the bone to determine the drilling starting point and angle. An optical real-time tracking is used to transfer position and rotation to the training system. Methods: A metamaterial already developed in previous work is further improved by use of a new unit cell. Thus, the amount of SUP within the volume can be increased and the tissue is softened further. In addition, the human anatomy is transferred to the entire hand model. A subcutaneous fat layer and penetration of air through pores into the volume simulate shiftability of skin layers. For optical tracking, a rotationally symmetrical marker attached to the phantom hand with corresponding reference marker is developed. In order to ensure trouble-free position transmission, various types of marker point applications are tested. Results: Several cuboid and forearm sample prints lead to a final 30 centimeter long hand model. The whole haptic phantom could be printed faultless within about 17 hours. The metamaterial consisting of the new unit cell results in an increased SUP share of 4.32\%. Validated by an expert surgeon study, this allows in combination with a displacement of the uppermost skin layer a good palpability of the bones. Tracking of the hand marker in dodecahedron design works trouble-free in conjunction with a reference marker attached to the worktop of the training system. Conclusions: In this work, an optically tracked and haptically correct phantom hand was developed using dual-material 3D printing, which can be easily integrated into a surgical training system.}, subject = {Handchirurgie}, language = {en} } @article{NoisserEigenbergerWeihereretal., author = {Noisser, Vivien and Eigenberger, Andreas and Weiherer, Maximilian and Seitz, Stephan and Prantl, Lukas and Br{\´e}bant, Vanessa}, title = {Surgery of congenital breast asymmetry - which objective parameter influences the subjective satisfaction with long-term results}, series = {Archives of Gynecology and Obstetrics}, journal = {Archives of Gynecology and Obstetrics}, publisher = {Springer Nature}, doi = {10.1007/s00404-021-06218-0}, pages = {8}, abstract = {Purpose Congenital breast asymmetry is a serious gynecological malformation for affected patients. The condition hits young women in puberty and is associated with socio-esthetic handicap, depression, and psychosexual problems. Surgical treatment is usually early in the patient's lifetime, so a long-term sustainable solution is important. Although postoperative outcome has been evaluated in several studies before, this study is the first to analyze which objective parameters have the greatest influence on subjective satisfaction with long-term results. Methods Thirty-four patients diagnosed with congenital breast asymmetry that underwent either lipofilling or implant therapy between the years of 2008 to 2019 were examined. On average, our collective comprised patients seven years after surgery. Data were mainly gathered through manual measurements, patient-reported outcome measures (Breast Q™), and breast volumetry based on 3D scans (Vectra® H2, Canfield Scientific). Results Among all analyzed parameters, only areolar diameter correlated significantly negatively with the subjective outcome satisfaction of the patient. Regarding the subjective assessment of postoperative satisfaction with similarity of the breasts, again the mean areolar diameter, but also the difference in areolar diameter and breast volume between the right and left breasts correlated significantly negatively. Conclusion Areolar diameter was revealed as being a significant factor influencing subjective long-term satisfaction in breast asymmetry patients. Moreover, 3D volumetry proves to be an effective tool to substantiate subjective patient assessments. Our findings may lead to further improvements to surgical planning and will be expanded in further studies.}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma}, series = {GuT}, volume = {68}, journal = {GuT}, number = {7}, publisher = {British Society of Gastroenterology}, doi = {10.1136/gutjnl-2018-317573}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-68}, pages = {1143 -- 1145}, abstract = {Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett's oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97\%/88\% (Augsburg data) and 92\%/100\% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94\%/80\% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management.}, subject = {Speiser{\"o}hrenkrebs}, language = {en} } @article{WoehlMaierGehmertetal., author = {W{\"o}hl, Rebecca and Maier, Johannes and Gehmert, Sebastian and Palm, Christoph and Riebschl{\"a}ger, Birgit and Nerlich, Michael and Huber, Michaela}, title = {3D Analysis of Osteosyntheses Material using semi-automated CT Segmentation}, series = {BMC Musculoskeletal Disorders}, volume = {19}, journal = {BMC Musculoskeletal Disorders}, publisher = {Springer Nature}, doi = {10.1186/s12891-018-1975-0}, pages = {1 -- 8}, abstract = {Backround Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non)locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation. Methods: This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed. Results During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60°, consisting of 30° extension and 30° flexion. While pain levels decreased significantly, 54\% of grip strength and 89\% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome. Conclusion Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers' promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate.}, subject = {Handchirurgie}, language = {en} } @article{SouzaJrPassosSantanaetal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Santana, Marcos Cleison S. and Mendel, Robert and Rauber, David and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Layer-selective deep representation to improve esophageal cancer classification}, series = {Medical \& Biological Engineering \& Computing}, volume = {62}, journal = {Medical \& Biological Engineering \& Computing}, publisher = {Springer Nature}, address = {Heidelberg}, doi = {10.1007/s11517-024-03142-8}, pages = {3355 -- 3372}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis.For this task, the deep learning techniques' black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett's esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem.}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Meinikheim, Michael and Byrne, Michael F. and Messmann, Helmut and Palm, Christoph}, title = {Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence}, series = {Endoscopy}, volume = {54}, journal = {Endoscopy}, number = {10}, edition = {E-Video}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-1704-7885}, pages = {1}, abstract = {The early diagnosis of cancer in Barrett's esophagus is crucial for improving the prognosis. However, identifying Barrett's esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett's esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3]. In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time.}, language = {en} } @article{KolevKirchgessnerHoubenetal., author = {Kolev, Kalin and Kirchgeßner, Norbert and Houben, Sebastian and Csisz{\´a}r, Agnes and Rubner, Wolfgang and Palm, Christoph and Eiben, Bj{\"o}rn and Merkel, Rudolf and Cremers, Daniel}, title = {A variational approach to vesicle membrane reconstruction from fluorescence imaging}, series = {Pattern Recognition}, volume = {44}, journal = {Pattern Recognition}, number = {12}, publisher = {Elsevier}, doi = {10.1016/j.patcog.2011.04.019}, pages = {2944 -- 2958}, abstract = {Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.}, subject = {Dreidimensionale Bildverarbeitung}, language = {en} } @article{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {ARTIFICIAL INTELLIGENCE (AI) - ASSISTED VESSEL AND TISSUE RECOGNITION IN THIRD-SPACE ENDOSCOPY}, series = {Endoscopy}, volume = {54}, journal = {Endoscopy}, number = {S01}, publisher = {Thieme}, doi = {10.1055/s-0042-1745037}, pages = {S175}, abstract = {Aims Third-space endoscopy procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex interventions with elevated risk of operator-dependent adverse events, such as intra-procedural bleeding and perforation. We aimed to design an artificial intelligence clinical decision support solution (AI-CDSS, "Smart ESD") for the detection and delineation of vessels, tissue structures, and instruments during third-space endoscopy procedures. Methods Twelve full-length third-space endoscopy videos were extracted from the Augsburg University Hospital database. 1686 frames were annotated for the following categories: Submucosal layer, blood vessels, electrosurgical knife and endoscopic instrument. A DeepLabv3+neural network with a 101-layer ResNet backbone was trained and validated internally. Finally, the ability of the AI system to detect visible vessels during ESD and POEM was determined on 24 separate video clips of 7 to 46 seconds duration and showing 33 predefined vessels. These video clips were also assessed by an expert in third-space endoscopy. Results Smart ESD showed a vessel detection rate (VDR) of 93.94\%, while an average of 1.87 false positive signals were recorded per minute. VDR of the expert endoscopist was 90.1\% with no false positive findings. On the internal validation data set using still images, the AI system demonstrated an Intersection over Union (IoU), mean Dice score and pixel accuracy of 63.47\%, 76.18\% and 86.61\%, respectively. Conclusions This is the first AI-CDSS aiming to mitigate operator-dependent limitations during third-space endoscopy. Further clinical trials are underway to better understand the role of AI in such procedures.}, language = {en} } @article{HartwigBerletCzempieletal., author = {Hartwig, Regine and Berlet, Maximilian and Czempiel, Tobias and Fuchtmann, Jonas and R{\"u}ckert, Tobias and Feussner, Hubertus and Wilhelm, Dirk}, title = {Bildbasierte Unterst{\"u}tzungsmethoden f{\"u}r die zuk{\"u}nftige Anwendung in der Chirurgie}, series = {Die Chirurgie}, volume = {93}, journal = {Die Chirurgie}, publisher = {Springer}, doi = {10.1007/s00104-022-01668-x}, pages = {956 -- 965}, abstract = {Hintergrund: Die Entwicklung assistiver Technologien wird in den kommenden Jahren nicht nur in der Chirurgie von zunehmender Bedeutung sein. Die Wahrnehmung der Istsituation stellt hierbei die Grundlage jeder autonomen Handlung dar. Hierf{\"u}r k{\"o}nnen unterschiedliche Sensorsysteme genutzt werden, wobei videobasierte Systeme ein besonderes Potenzial aufweisen. Methode: Anhand von Literaturangaben und auf Basis eigener Forschungsarbeiten werden zentrale Aspekte bildbasierter Unterst{\"u}tzungssysteme f{\"u}r die Chirurgie dargestellt. Hierbei wird deren Potenzial, aber auch die Limitationen der Methoden erl{\"a}utert. Ergebnisse: Eine etablierte Anwendung stellt die Phasendetektion chirurgischer Eingriffe dar, f{\"u}r die Operationsvideos mittels neuronaler Netzwerke analysiert werden. Durch eine zeitlich gest{\"u}tzte und transformative Analyse konnten die Ergebnisse der Pr{\"a}diktion j{\"u}ngst deutlich verbessert werden. Aber auch robotische Kameraf{\"u}hrungssysteme nutzen Bilddaten, um das Laparoskop zuk{\"u}nftig autonom zu navigieren. Um die Zuverl{\"a}ssigkeit an die hohen Anforderungen in der Chirurgie anzugleichen, m{\"u}ssen diese jedoch durch zus{\"a}tzliche Informationen erg{\"a}nzt werden. Ein vergleichbarer multimodaler Ansatz wurde bereits f{\"u}r die Navigation und Lokalisation bei laparoskopischen Eingriffen umgesetzt. Hierzu werden Videodaten mittels verschiedener Methoden analysiert und diese Ergebnisse mit anderen Sensormodalit{\"a}ten fusioniert. Diskussion: Bildbasierte Unterst{\"u}tzungsmethoden sind bereits f{\"u}r diverse Aufgaben verf{\"u}gbar und stellen einen wichtigen Aspekt f{\"u}r die Chirurgie der Zukunft dar. Um hier jedoch zuverl{\"a}ssig und f{\"u}r autonome Funktionen eingesetzt werden zu k{\"o}nnen, m{\"u}ssen sie zuk{\"u}nftig in multimodale Ans{\"a}tze eingebettet werden, um die erforderliche Sicherheit bieten zu k{\"o}nnen.}, language = {de} } @article{SouzaJrPachecoPassosetal., author = {Souza Jr., Luis Antonio de and Pacheco, Andr{\´e} G.C. and Passos, Leandro A. and Santana, Marcos Cleison S. and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett's esophagus}, series = {Neural Computing and Applications}, volume = {36}, journal = {Neural Computing and Applications}, publisher = {Springer}, address = {London}, doi = {10.1007/s00521-024-09615-z}, pages = {10445 -- 10459}, abstract = {Limitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett's esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95\% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer.}, subject = {Deep Learning}, language = {en} } @article{SouzaPachecodeSouzaetal., author = {Souza, Luis A. and Pacheco, Andr{\´e} G.C. and de Souza, Alberto F. and Oliveira-Santos, Thiago and Badue, Claudine and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {TransConv: a lightweight architecture based on transformers and convolutional neural networks for adenocarcinoma and Barrett's esophagus identification}, series = {Neural Computing and Applications}, journal = {Neural Computing and Applications}, number = {37}, publisher = {Springer}, doi = {10.1007/s00521-025-11299-y}, pages = {15535 -- 15546}, abstract = {Barrett's esophagus, also known as BE, is commonly associated with repeated exposure to stomach acid. If not treated properly, it may evolve into esophageal adenocarcinoma, aka esophageal cancer. This paper proposes TransConv, a hybrid architecture that benefits from features learned by pre-trained vision transformers (ViTs) and convolutional neural networks (CNNs), followed by a shallow neural network composed of three normalizations, ReLU activations, and fully connected layers, and a SoftMax head to distinguish between BE and esophageal cancer. TransConv is designed to be training-lightweight, and for the ViT and CNN backbone models, weights are kept frozen during training, i.e., the primary goal of TransConv is to learn the weights of the fully connected layer from both backbones only, avoiding the burden of updating their weights but still learning their final descriptions for the lightweight convolutional model. We report promising results with low computational training costs in two datasets, one public and another private. From our achievements, TransConv was able to deliver balanced accuracy results around 85\% and 86\% for each evaluated dataset, respectively, in a design that required only 50 epochs of model training, a very reduced number compared to state-of-the-art conducted studies in the same domain.}, language = {en} } @article{HammerNunesHammeretal., author = {Hammer, Simone and Nunes, Danilo Weber and Hammer, Michael and Zeman, Florian and Akers, Michael and G{\"o}tz, Andrea and Balla, Annika and Doppler, Michael Christian and Fellner, Claudia and Da Platz Batista Silva, Natascha and Thurn, Sylvia and Verloh, Niklas and Stroszczynski, Christian and Wohlgemuth, Walter Alexander and Palm, Christoph and Uller, Wibke}, title = {Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI}, series = {Clinical hemorheology and microcirculation}, journal = {Clinical hemorheology and microcirculation}, edition = {Pre-press}, publisher = {IOP Press}, doi = {10.3233/CH-232071}, pages = {1 -- 15}, abstract = {BACKGROUND Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI. METHODS 527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN. RESULTS Compared with the expert radiologists the CNN achieved similar accuracy (92\% vs. 97\%, p = 0.11), sensitivity (80\% vs. 93\%, p = 0.16) and specificity (97\% vs. 100\%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97\% vs. 80\%, p <  0.001; 92\% vs. 77\%, p <  0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN. CONCLUSIONS Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.}, language = {en} } @article{RueckertRauberMaerkletal., author = {Rueckert, Tobias and Rauber, David and Maerkl, Raphaela and Klausmann, Leonard and Yildiran, Suemeyye R. and Gutbrod, Max and Nunes, Danilo Weber and Moreno, Alvaro Fernandez and Luengo, Imanol and Stoyanov, Danail and Toussaint, Nicolas and Cho, Enki and Kim, Hyeon Bae and Choo, Oh Sung and Kim, Ka Young and Kim, Seong Tae and Arantes, Gon{\c{c}}alo and Song, Kehan and Zhu, Jianjun and Xiong, Junchen and Lin, Tingyi and Kikuchi, Shunsuke and Matsuzaki, Hiroki and Kouno, Atsushi and Manesco, Jo{\~a}o Renato Ribeiro and Papa, Jo{\~a}o Paulo and Choi, Tae-Min and Jeong, Tae Kyeong and Park, Juyoun and Alabi, Oluwatosin and Wei, Meng and Vercauteren, Tom and Wu, Runzhi and Xu, Mengya and Wang, An and Bai, Long and Ren, Hongliang and Yamlahi, Amine and Hennighausen, Jakob and Maier-Hein, Lena and Kondo, Satoshi and Kasai, Satoshi and Hirasawa, Kousuke and Yang, Shu and Wang, Yihui and Chen, Hao and Rodr{\´i}guez, Santiago and Aparicio, Nicol{\´a}s and Manrique, Leonardo and Palm, Christoph and Wilhelm, Dirk and Feussner, Hubertus and Rueckert, Daniel and Speidel, Stefanie and Nasirihaghighi, Sahar and Al Khalil, Yasmina and Li, Yiping and Arbel{\´a}ez, Pablo and Ayobi, Nicol{\´a}s and Hosie, Olivia and Lyons, Juan Camilo}, title = {Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge}, series = {Medical Image Analysis}, volume = {109}, journal = {Medical Image Analysis}, publisher = {Elsevier}, issn = {1361-8415}, doi = {10.1016/j.media.2026.103945}, pages = {31}, abstract = {Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.}, language = {en} } @article{deSouzaJuniorPachecoOliveiradosSantosetal., author = {de Souza J{\´u}nior, Luis Antonio and Pacheco, Andr{\´e} Georghton Cardoso and Oliveira dos Santos, Thiago and Fogos da Rocha, Wyctor and Bouzon, Pedro Henrique and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {LiwTERM-r: a Revised Lightweight Transformer-based Model for Multimodal Skin Lesion Detection Robust to Incomplete Input}, series = {Journal of the Brazilian Computer Society}, volume = {32}, journal = {Journal of the Brazilian Computer Society}, number = {1}, publisher = {Brazilian Computer Society}, doi = {10.5753/jbcs.2026.5871}, pages = {11}, abstract = {As the most common type of cancer in the world, skin cancer accounts for approximately 30\% of all diagnosed tumor-based lesions. Early diagnosis can reduce mortality and prevent disfiguring in different skin regions. With the application of machine learning techniques in recent years, especially deep learning, promising results in this task could be achieved, presenting studies demonstrating that the combination of patients' clinical anamneses and images of the injured lesion is essential for improving the correct classification of skin lesions. Despite that, meaningful use of anamneses with multiple collected images of the same skin lesion is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to solve the skin lesion classification problem by employing a lightweight transformer model that is robust to missing clinical information input. As a main hypothesis, models can be fed by multiple images from different sources as input along with clinical anamneses from the patient's historical evaluations, leading to a more factual and trustworthy diagnosis. Our model deals with the not-trivial task of combining images and clinical information concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources or even all the information from the anamneses but still presents competitive classification results.}, language = {en} } @article{WallnerGutbrodRauberetal., author = {Wallner, M. and Gutbrod, Max and Rauber, David and Ebigbo, Alanna and Probst, Andreas and Palm, Christoph and Messmann, Helmut and Roser, David}, title = {KI-gest{\"u}tzte Detektion und Segmentierung von Magenkarzinomen in westlichen endoskopischen Bilddaten anhand eines fine-tuned Deep-Learning Ansatzes}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {64}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {03}, publisher = {Thieme}, doi = {10.1055/s-0046-1817751}, pages = {e64 -- e65}, abstract = {Diese vorl{\"a}ufige monozentrische Studie zeigt, dass ein aus einem Barrett-{\"O}sophagus-KI-System feinjustiertes Deep-Learning-Modell Magenkarzinome in westlichen multimodalen endoskopischen Bilddaten zuverl{\"a}ssig detektieren und pr{\"a}zise segmentieren kann. Die hohe Segmentierungsgenauigkeit und Detektionssensitivit{\"a}t {\"u}ber verschiedene Bildmodalit{\"a}ten hinweg unterstreichen die Machbarkeit eines pathologiegest{\"u}tzten KI-Ansatzes auch in einer westlichen Niedriginzidenzpopulation. Aufgrund der ausschließlichen Verwendung von Bildern mit sichtbaren Tumoren lassen sich keine Aussagen zur Spezifit{\"a}t treffen; eine {\"U}bertragbarkeit auf Screening- oder Mischkollektive ist daher limitiert. Weitere Studien mit a) gr{\"o}ßerem Datensatz inklusive Videodaten, b) externer Validierung an einer multizentrischen westlichen Kohorte, sowie c) Anwendung und Pr{\"u}fung an nicht-neoplastischen Vergleichsbildern oder anderen Pathologien sind erforderlich. Nach unserem Kenntnisstand z{\"a}hlt dieses System zu den ersten in einer westlichen Population entwickelten endoskopischen KI-Ans{\"a}tzen zur Magenkarzinomdetektion, und zu wenigen, die vollst{\"a}ndige ESD-pr{\"a}paratbasierte Referenzdaten f{\"u}r Training und Validierung nutzen.}, language = {de} } @article{WeiherervonRiedheimBrebantetal., author = {Weiherer, Maximilian and von Riedheim, Antonia and Br{\´e}bant, Vanessa and Egger, Bernhard and Palm, Christoph}, title = {Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos}, series = {Machine Learning for Biomedical Imaging (MELBA)}, journal = {Machine Learning for Biomedical Imaging (MELBA)}, number = {MELBA-BVM 2025 Special Issue}, publisher = {Melba}, doi = {10.59275/j.melba.2026-8b23}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-89791}, pages = {95 -- 114}, abstract = {We present a neural parametric 3D breast shape model and, based on this model, introduce a low-cost and accessible 3D surface reconstruction pipeline capable of recovering accurate breast geometry from a monocular RGB video. In contrast to widely used, commercially available yet expensive 3D breast scanning solutions and existing low-cost alternatives, our method requires neither specialized hardware nor proprietary software and can be used with any device that is able to record RGB videos. The key building blocks of our pipeline are a state-of-the-art, off-the-shelf Structure-from-Motion pipeline, paired with a parametric breast model for robust surface reconstruction. Our model, similarly to the recently proposed implicit Regensburg Breast Shape Model (iRBSM), leverages implicit neural representations to model breast shapes. However, unlike the iRBSM, which employs a single global neural Signed Distance Function (SDF), our approach—inspired by recent state-of-the-art face models—decomposes the implicit breast domain into multiple smaller regions, each represented by a local neural SDF anchored at anatomical landmark positions. When incorporated into our surface reconstruction pipeline, the proposed model, dubbed liRBSM (short for localized iRBSM), significantly outperforms the iRBSM in terms of reconstruction quality, yielding more detailed surface reconstruction than its global counterpart. Overall, we find that the introduced pipeline is able to recover high-quality and metrically correct 3D breast geometry within an error margin of less than 2 mm. Our method is fast (requires less than six minutes), fully transparent and open-source, and together with the model publicly available at https://rbsm.re-mic.de/local-implicit.}, language = {en} }