@article{DesernoHandelsMaierHeinetal., author = {Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Mersmann, Sven and Palm, Christoph and Tolxdorff, Thomas and Wagenknecht, Gudrun and Wittenberg, Thomas}, title = {Viewpoints on Medical Image Processing}, series = {Current Medical Imaging Reviews}, volume = {9}, journal = {Current Medical Imaging Reviews}, number = {2}, doi = {10.2174/1573405611309020002}, pages = {79 -- 88}, abstract = {Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.}, subject = {Bildgebendes Verfahren}, language = {en} } @inproceedings{SouzaJrHookPapaetal., author = {Souza Jr., Luis Antonio de and Hook, Christian and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Barrett's Esophagus Analysis Using SURF Features}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_34}, pages = {141 -- 146}, abstract = {The development of adenocarcinoma in Barrett's esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being "cancerous", the other half as "non-cancerous". Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{WoehlHuberLoibletal., author = {W{\"o}hl, Rebecca and Huber, Michaela and Loibl, Markus and Riebschl{\"a}ger, Birgit and Nerlich, Michael and Palm, Christoph}, title = {The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_30}, pages = {122 -- 127}, abstract = {A new protocol for testing osteosynthesis material postoperatively combining semi-automated segmentation and 3D analysis of surface meshes is proposed. By various steps of transformation and measuring, objective data can be collected. In this study the specifications of a locking plate used for mediocarpal arthrodesis of the wrist were examined. The results show, that union of the lunate, triquetrum, hamate and capitate was achieved and that the plate is comparable to coexisting arthrodesis systems. Additionally, it was shown, that the complications detected correlate to the clinical outcome. In synopsis, this protocol is considered beneficial and should be taken into account in further studies.}, subject = {Osteosynthese}, language = {en} } @inproceedings{SouzaJrAfonsoPalmetal., author = {Souza Jr., Luis Antonio de and Afonso, Luis Claudio Sugi and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's Esophagus Identification Using Optimum-Path Forest}, series = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, booktitle = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, doi = {10.1109/SIBGRAPI.2017.47}, pages = {308 -- 314}, abstract = {Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @misc{SchroederSemmelmannSiegmundetal., author = {Schroeder, Josef A. and Semmelmann, Matthias and Siegmund, Heiko and Grafe, Claudia and Evert, Matthias and Palm, Christoph}, title = {Improved interactive computer-assisted approach for evaluation of ultrastructural cilia abnormalities}, series = {Ultrastructural Pathology}, volume = {41}, journal = {Ultrastructural Pathology}, number = {1}, doi = {10.1080/01913123.2016.1270978}, pages = {112 -- 113}, subject = {Zilie}, language = {en} } @inproceedings{PalmSiegmundSemmelmannetal., author = {Palm, Christoph and Siegmund, Heiko and Semmelmann, Matthias and Grafe, Claudia and Evert, Matthias and Schroeder, Josef A.}, title = {Interactive Computer-assisted Approach for Evaluation of Ultrastructural Cilia Abnormalities}, series = {Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March, SPIE Proceedings 97853N, 2016, ISBN 9781510600201}, booktitle = {Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February - 3 March, SPIE Proceedings 97853N, 2016, ISBN 9781510600201}, doi = {10.1117/12.2214976}, pages = {7}, abstract = {Introduction - Diagnosis of abnormal cilia function is based on ultrastructural analysis of axoneme defects, especialy the features of inner and outer dynein arms which are the motors of ciliar motility. Sub-optimal biopsy material, methodical, and intrinsic electron microscopy factors pose difficulty in ciliary defects evaluation. We present a computer-assisted approach based on state-of-the-art image analysis and object recognition methods yielding a time-saving and efficient diagnosis of cilia dysfunction. Method - The presented approach is based on a pipeline of basal image processing methods like smoothing, thresholding and ellipse fitting. However, integration of application specific knowledge results in robust segmentations even in cases of image artifacts. The method is build hierarchically starting with the detection of cilia within the image, followed by the detection of nine doublets within each analyzable cilium, and ending with the detection of dynein arms of each doublet. The process is concluded by a rough classification of the dynein arms as basis for a computer-assisted diagnosis. Additionally, the interaction possibilities are designed in a way, that the results are still reproducible given the completion report. Results - A qualitative evaluation showed reasonable detection results for cilia, doublets and dynein arms. However, since a ground truth is missing, the variation of the computer-assisted diagnosis should be within the subjective bias of human diagnosticians. The results of a first quantitative evaluation with five human experts and six images with 12 analyzable cilia showed, that with default parameterization 91.6\% of the cilia and 98\% of the doublets were found. The computer-assisted approach rated 66\% of those inner and outer dynein arms correct, where all human experts agree. However, especially the quality of the dynein arm classification may be improved in future work.}, subject = {Zilie}, language = {en} } @inproceedings{WeberDoenitzBrawanskietal., author = {Weber, Joachim and Doenitz, Christian and Brawanski, Alexander and Palm, Christoph}, title = {Data-Parallel MRI Brain Segmentation in Clinicial Use}, series = {Bildverarbeitung f{\"u}r die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-46224-9_67}, pages = {389 -- 394}, abstract = {Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB's Segmentation Library - FMRIB's Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results.}, subject = {Kernspintomografie}, language = {en} } @inproceedings{MendelEbigboProbstetal., author = {Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph}, title = {Barrett's Esophagus Analysis Using Convolutional Neural Networks}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_23}, pages = {80 -- 85}, abstract = {We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain.}, subject = {Speiser{\"o}hrenkrebs}, language = {en} } @inproceedings{EixelbergerWittenbergPerretetal., author = {Eixelberger, Thomas and Wittenberg, Thomas and Perret, Jerome and Katzky, Uwe and Simon, Martina and Schmitt-R{\"u}th, Stephanie and Hofer, Mathias and Sorge, M. and Jacob, R. and Engel, Felix B. and Gostian, A. and Palm, Christoph and Franz, Daniela}, title = {A haptic model for virtual petrosal bone milling}, series = {17. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC2018), Tagungsband, 2018, Leipzig, 13.-15. September}, volume = {17}, booktitle = {17. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC2018), Tagungsband, 2018, Leipzig, 13.-15. September}, pages = {214 -- 219}, abstract = {Virtual training of bone milling requires realtime and realistic haptics of the interaction between the "virtual mill" and a "virtual bone". We propose an exponential abrasion model between a virtual one and the mill bit and combine it with a coarse representation of the virtual bone and the mill shaft for collision detection using the Bullet Physics Engine. We compare our exponential abrasion model to a widely used linear abrasion model and evaluate it quantitatively and qualitatively. The evaluation results show, that we can provide virtual milling in real-time, with an abrasion behavior similar to that proposed in the literature and with a realistic feeling of five different surgeons.}, subject = {Osteosynthese}, 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} } @inproceedings{MaierHuberKatzkyetal., author = {Maier, Johannes and Huber, Michaela and Katzky, Uwe and Perret, Jerome and Wittenberg, Thomas and Palm, Christoph}, title = {Force-Feedback-assisted Bone Drilling Simulation Based on CT Data}, series = {Bildverarbeitung f{\"u}r die Medizin 2018; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 11. bis 13. M{\"a}rz 2018 in Erlangen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2018; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 11. bis 13. M{\"a}rz 2018 in Erlangen}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-56537-7_78}, pages = {291 -- 296}, abstract = {In order to fix a fracture using minimally invasive surgery approaches, surgeons are drilling complex and tiny bones with a 2 dimensional X-ray as single imaging modality in the operating room. Our novel haptic force-feedback and visual assisted training system will potentially help hand surgeons to learn the drilling procedure in a realistic visual environment. Within the simulation, the collision detection as well as the interaction between virtual drill, bone voxels and surfaces are important. In this work, the chai3d collision detection and force calculation algorithms are combined with a physics engine to simulate the bone drilling process. The chosen Bullet-Physics-Engine provides a stable simulation of rigid bodies, if the collision model of the drill and the tool holder is generated as a compound shape. Three haptic points are added to the K-wire tip for removing single voxels from the bone. For the drilling process three modes are proposed to emulate the different phases of drilling in restricting the movement of a haptic device.}, subject = {Handchirurgie}, language = {en} } @article{SouzaJrPalmMendeletal., author = {Souza Jr., Luis Antonio de and Palm, Christoph and Mendel, Robert and Hook, Christian and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Weber, Silke A. T. and Papa, Jo{\~a}o Paulo}, title = {A survey on Barrett's esophagus analysis using machine learning}, series = {Computers in Biology and Medicine}, volume = {96}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2018.03.014}, pages = {203 -- 213}, abstract = {This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} }