@unpublished{WeihererEigenbergerBrebantetal., author = {Weiherer, Maximilian and Eigenberger, Andreas and Br{\´e}bant, Vanessa and Prantl, Lukas and Palm, Christoph}, title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}, pages = {15}, abstract = {We present the Regensburg Breast Shape Model (RBSM) - a 3D statistical shape model of the female breast built from 110 breast scans, and the first ever publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. In order to weaken the strong coupling between breast and thorax, 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 for the RBSM. Ultimately, our model is seen as a first step towards combining physically motivated deformable models of the breast and statistical approaches in order to enable more realistic surgical outcome simulation.}, language = {en} } @inproceedings{PalmMetzlerMohametal., author = {Palm, Christoph and Metzler, V. and Moham, B. and Dieker, O. and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Co-Occurrence Matrizen zur Texturklassifikation in Vektorbildern}, series = {Bildverarbeitung f{\"u}r die Medizin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin}, editor = {Evers, H. and Glombitza, G. and Lehmann, Thomas M. and Meinzer, H.-P.}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-60125-5_69}, pages = {367 -- 371}, abstract = {Statistische Eigenschaften nat{\"u}rlicher Grauwerttexturen werden mit Co-Occurrence Matrizen, basierend auf der Grauwertstatistik zweiter Ordnung, modelliert. Die Matrix gibt dann die apriori Wahrscheinlichkeiten aller Grauwertpaare an. Da in der medizinischen Bildverarbeitung verst{\"a}rkt Multispektralbilder ausgewertet werden, wird das bekannte Konzept hier auf beliebige Vektorbilder erweitert. Dadurch kann bei der Texturklassifikation die zur Verf{\"u}gung stehende Information vollst{\"a}ndig genutzt werden. Insbesondere zur Detektion von Farbtexturen ist dieser Ansatz geeignet, da Wertepaare unterschiedlicher Spektralebenen ausgewertet werden k{\"o}nnen. Ebenso kann die Methode auch bei der Multiskalendekomposition von Intensit{\"a}tsbildern zur Verbesserung der Texturerkennung beitragen. Die in den Matrizen entstehenden Muster lassen dann {\"u}ber die Extraktion geeigneter Texturdeskriptoren R{\"u}ckschl{\"u}sse auf die Texturen des Bildes zu.}, language = {de} } @inproceedings{PalmPelkmannLehmannetal., author = {Palm, Christoph and Pelkmann, Annegret and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Distortion Correction of Laryngoscopic Images}, series = {Advances in quantitative laryngoscopy, voice and speech research, Proceedings of the 3rd international workshop Aachen, RWTH}, booktitle = {Advances in quantitative laryngoscopy, voice and speech research, Proceedings of the 3rd international workshop Aachen, RWTH}, pages = {117 -- 125}, abstract = {Laryngoscopic images of the vocal tract are used for diagnostic purposes. Quantitative mea-surements like changes of the glottis size or the surface of the vocal cords during an image sequence can be helpful to describe the healing process or to compare the findings of diffe-rent patients. Typically the endoscopic images are circulary symmetric distorted (barrel di-stortion). Therefore measurements of geometric dimensions depend on the object´s position in the image. In this paper an algorithm is presented which allows the computation of the translational invariant "real" object size by correcting the image distortion without using additional calibration of the optical environment.}, language = {en} } @inproceedings{PalmNeuschaeferRubeLehmannetal., author = {Palm, Christoph and Neuschaefer-Rube, C. and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Wissensbasierte Bewegungskompensation in aktiven Konturmodellen}, series = {Bildverarbeitung f{\"u}r die Medizin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin}, editor = {Evers, H. and Glombitza, G. and Lehmann, Thomas M. and Meinzer, H.-P.}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-60125-5_2}, pages = {8 -- 12}, abstract = {Zur Analyse von Lippenbewegungsabl{\"a}ufen wird ein aktives Konturmodell eingesetzt. Probleme bereitet die hohe Sprechgeschwindigkeit, die in star ken Objektverschiebungen result iert und bislang nicht durch eine alleinige Konturanpassung kompensiert werden kann. In diesem Beitrag werden die klassischen aktiven Konturmodelle um eine Vorjustierung der Grobkonturen erweitert, die eine energiebasierte Konturanpassung erst m{\"o}glich macht. Die Sch{\"a}tzung der Verschiebung zur Vorjustierung basiert auf dem Gradientenbild und einem pr{\"a}dikatenlogisch formulierten Regelwerk, das Annahmen und Nebenbedingungen als Wissensbasis enth{\"a}lt. Mit Hilfe dieser Erweiterungen ist eine automatisierte Konturverfolgung der Lippen m{\"o}glich.}, language = {de} } @inproceedings{PalmSchollLehmannetal., author = {Palm, Christoph and Scholl, Ingrid and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Quantitative Farbmessung in laryngoskopischen Bildern}, series = {Bildverarbeitung f{\"u}r die Medizin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin}, editor = {Lehmann, Thomas M. and Metzler, V. and Spitzer, Klaus and Tolxdorff, Thomas}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-58775-7_81}, pages = {412 -- 416}, abstract = {Quantitative Farbmessungen sollen die Diagnostik laryngealer Erkrankungen unterst{\"u}tzen. Dabei wird der Farbeindruck nicht nur durch die Reflexionseigenschaften des Gewebes sondern auch durch die Farbe der verwendeten Lichtquelle beeinflußt. Der hier vorgestellte Farbkonstanz-Algorithmus basiert auf dem dichromatischen Reflexionsmodell und liefert eine pixelweise Trennung des Farbbildes in seine beiden Faxbanteile. Die K{\"o}rperfarbe entspricht dabei der gewebespezifischen Reflexion, die Oberf{\"a}chenfarbe der Strahlung der Lichtquelle.}, language = {de} } @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} } @inproceedings{MendelSouzaJrRauberetal., author = {Mendel, Robert and Souza Jr., Luis Antonio de and Rauber, David and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Semi-supervised Segmentation Based on Error-Correcting Supervision}, series = {Computer vision - ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX}, booktitle = {Computer vision - ECCV 2020: 16th European conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-58525-9}, doi = {10.1007/978-3-030-58526-6_9}, pages = {141 -- 157}, abstract = {Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network. The secondary correction network learns on the labeled data to optimally spot correct predictions, as well as to amend incorrect ones. As auxiliary regularization term, the corrector directly influences the supervised training of the segmentation network. On unlabeled data, the output of the correction network is essential to create a proxy for the unknown truth. The corrector's output is combined with the segmentation network's prediction to form the new target. We propose a loss function that incorporates both the pseudo-labels as well as the predictive certainty of the correction network. Our approach can easily be added to supervised segmentation models. We show consistent improvements over a supervised baseline on experiments on both the Pascal VOC 2012 and the Cityscapes datasets with varying amounts of labeled data.}, subject = {Semi-Supervised Learning}, language = {en} } @inproceedings{PalmSchollLehmannetal., author = {Palm, Christoph and Scholl, Ingrid and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Trennung von diffuser und spiegelnder Reflexion in Farbbildern des Larynx zur Untersuchung von Farb- und Formmerkmalen der Stimmlippen}, series = {Bildverarbeitung f{\"u}r die Medizin. Algorithmen, Systeme, Anwendungen. Proceedings des Aachener Workshops am 8. und 9. November 1996}, booktitle = {Bildverarbeitung f{\"u}r die Medizin. Algorithmen, Systeme, Anwendungen. Proceedings des Aachener Workshops am 8. und 9. November 1996}, editor = {Lehmann, Thomas M. and Scholl, Ingrid and Spitzer, Klaus}, publisher = {Verlag der Augustinus-Buchhandlung}, address = {Aachen}, pages = {229 -- 234}, abstract = {Zur diagnostischen Unterst{\"u}tzung bei der Befundung laryngealer Erkrankungen soll eine Farb- und Formanalyse der Stimmlippen durchgef{\"u}hrt werden. In diesem Beitrag wird ein Verfahren zur Trennung der spiegelnden und diffusen Reflexionsanteile in Farbbildern des Larynx vorgestellt. Die Farbe der diffusen Komponente entspricht dabei der beleuchtungsunabh{\"a}ngigen Objektfarbe, w{\"a}hrend deren Wichtungsfaktoren als Eingabe f{\"u}r Shape-from-Shading-Verfahren zur Oberfl{\"a}chenrekonstruktion dienen.}, subject = {Laryngoskopie}, language = {de} } @inproceedings{HassanIlgnerPalmetal., author = {Hassan, H. and Ilgner, Justus F. R. and Palm, Christoph and Lehmann, Thomas M. and Spitzer, Klaus and Westhofen, Martin}, title = {Objective Judgement of Endoscopic Laryngeal Images}, series = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Proceedings of the 3rd International Workshop, RWTH Aachen}, booktitle = {Advances in Quantitative Laryngoscopy, Voice and Speech Research, Proceedings of the 3rd International Workshop, RWTH Aachen}, editor = {Lehmann, Thomas M. and Spitzer, Klaus and Tolxdorff, Thomas}, pages = {135 -- 142}, abstract = {Video Documentation of endoscopic findings simplifies diagnostic counseling of the patient and aids pre-operative discussion among the medical team. Judgment of such images is still subjective and can not give a quantitative evaluation of the disease process regarding diagnosis or response to treatment. Modern treatment of early laryngeal cancer with laserablation requires intensive follow up and frequent direct laryngoscopy under general anesthesia with blind biopsies to detect any tumor residual or recurrence. Inflammatory conditions of the larynx are frequently confused with other causes of dysphonia. Mapping anddigital analysis of the documented image will suggest the tumor site and avoids undue blind biopsies under anesthesia. However, varying illumination results in different colors reflected from the same object. To achieve quantitative analysis, color constancy has to be assured. Inthis paper, the environment is presented which allow the objective judgment of larngoscopies.}, language = {en} } @inproceedings{SchollPalmSovakaretal., author = {Scholl, Ingrid and Palm, Christoph and Sovakar, Abhijit and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Quantitative Analyse der Stimmlippen}, series = {5. Workshop Digitale Bildverarbeitung in der Medizin, Universit{\"a}t Freiburg, 10.-11. M{\"a}rz 1997}, booktitle = {5. Workshop Digitale Bildverarbeitung in der Medizin, Universit{\"a}t Freiburg, 10.-11. M{\"a}rz 1997}, editor = {Arnolds, B. and Mueller, H. and Saupe, D. and Tolxdorff, Thomas}, pages = {81 -- 86}, language = {de} }