Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Darko Štern"'
Publikováno v:
Machine Learning for Biomedical Imaging. 1:1-27
In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations. To model the annotation ambiguities of the training dataset, we p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c26165fa03120d6d84e2fbb80f7fa57d
https://doi.org/10.59275/j.melba.2021-77a7
https://doi.org/10.59275/j.melba.2021-77a7
Autor:
Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Congcong Wang, Fan Zhang, Yu Wang, Yinan Xu, Shuiping Gou, Franz Thaler, Christian Payer, Darko Štern, Edward G.A. Henderson, Dónal M. McSweeney, Andrew Green, Price Jackson, Lachlan McIntosh, Quoc-Cuong Nguyen, Abdul Qayyum, Pierre-Henri Conze, Ziyan Huang, Ziqi Zhou, Deng-Ping Fan, Huan Xiong, Guoqiang Dong, Qiongjie Zhu, Jian He, Xiaoping Yang
Publikováno v:
Medical Image Analysis. 82:102616
Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad0c37b1ff2da0913a4fe36313e3e6f4
https://doi.org/10.1016/j.media.2022.102616
https://doi.org/10.1016/j.media.2022.102616
Publikováno v:
Medical Image Analysis. 54:207-219
In many medical image analysis applications, often only a limited amount of training data is available, which makes training of convolutional neural networks (CNNs) challenging. In this work on anatomical landmark localization, we propose a CNN archi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7eb62fff498752a26899bf207c59cca7
https://doi.org/10.1016/j.media.2019.03.007
https://doi.org/10.1016/j.media.2019.03.007
Autor:
Mohanasankar Sivaprakasam, Timyoas Yeah, Tao Jiang, Xin Wang, Dalong Cheng, Manish Sahu, Maodong Chen, Sebastian Lehnert, Alexander Valentinitsch, Dong Yang, Nicolas Boutry, Shangliang Xu, Johannes C. Paetzold, Alexander Tack, Yujin Hu, Kevin W. Brown, Marilia Lirio, Malek El Husseini, Xu Liming, Darko Štern, Nikolas Lessmann, Suprosanna Shit, Tianfu Wang, Alexandre Kirszenberg, Martin Urschler, Daguang Xu, Feng Hou, Laurence E. Court, Raymond P. Mumme, Maximilian T. Löffler, Sai Ho Ling, Stefan Zachow, Zheng Xiangshang, Markus Rempfler, Yiwei Bai, Elodie Puybareau, Li-Wen Wang, Nicolás Pérez de Olaguer, Moritz Ehlke, Tamaz Amiranashvili, Di Chen, Christoph Angerman, Chan Zeng, Zixun Huang, Jiri Chmelik, Giles Tetteh, Hongwei Li, Jan S. Kirschke, Heiko Ramm, Amirhossein Bayat, Björn H. Menze, Ivan Ezhov, Jan Kukačka, Anjany Sekuboyina, Chenhang He, Ben Glocker, Tucker Netherton, Hans Liebl, Zhiqiang He, Roman Jakubicek, Christian Payer, Felix Ambellan, Supriti Mulay, Lê Duy Huỳnh, Brandon H. Rapazzo, Xinjun Ma, Amber Zhang, Hans Lamecker, Benedikt Wiestler
Publikováno v:
Med. Image Anal. 73:102166 (2021)
Medical Image Analysis, 73
Medical Image Analysis, 73
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e4597e82cc93563d7ce7d43efb18451
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62736
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62736
Autor:
Anjany Sekuboyina, Darko Štern, Martin Urschler, Christian Payer, Jan S. Kirschke, Amirhossein Bayat, Johannes C. Paetzold, Bjoern H. Menze
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020-23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597245
MICCAI (6)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597245
MICCAI (6)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ceafa3c4fe0ff1e367a2d17899242bb
Publikováno v:
VISIGRAPP (5: VISAPP)
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6bae3088533c67c8c96784c9edd8ed45
https://doi.org/10.5220/0008975201240133
https://doi.org/10.5220/0008975201240133
Autor:
Jurgen Fripp, Martin Urschler, Maria Wimmer, Hao Chen, Hui Cheng, Shuo Li, Chunliang Wang, Robert Korez, Daniel Forsberg, Dieter Felsenberg, Gabriele Armbrecht, Tomaž Vrtovec, Darko Štern, Pheng-Ann Heng, Isabel Lŏpez Andrade, Alexey A. Novikov, Qi Dou, Hugo Hutt, Richard M. Everson, Bulat Ibragimov, Ben Glocker, Guoyan Zheng, Daniel L. Belavý, Ales Neubert, Chengwen Chu, Judith R. Meakin
Publikováno v:
Medical Image Analysis. 35:327-344
The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d90097824dea84f5212a4d170612946a
https://doi.org/10.1016/j.media.2016.08.005
https://doi.org/10.1016/j.media.2016.08.005
Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge
Autor:
Thierry Brouard, Qianqian Tong, Mattias P. Heinrich, Guoyan Zheng, Jennifer Keegan, Pheng-Ann Heng, Jean-Yves Ramel, Darko Štern, Weixin Si, Guodong Zeng, Zenglin Shi, Chengjia Wang, David N. Firmin, Xin Yang, Chenchen Sun, Örjan Smedby, Martin Urschler, Lei Li, David E. Newby, Chunliang Wang, Ulas Bagci, Julien Oster, Aliasghar Mortazi, Christian Payer, Raad Mohiaddin, Kawal Rhode, Tom MacGillivray, Xiangyun Liao, Sebastien Ourselin, Gaetan Galisot, Guanyu Yang, Xiahai Zhuang, Guang Yang, Cheng Bian
Publikováno v:
Medical Image Analysis
Medical Image Analysis, Elsevier, 2019, 58, pp.101537. ⟨10.1016/j.media.2019.101537⟩
Zhuang, X, Li, L, Payer, C, Štern, D, Urschler, M, Heinrich, M P, Oster, J, Wang, C, Smedby, Ö, Bian, C, Yang, X, Heng, P-A, Mortazi, A, Bagci, U, Yang, G, Sun, C, Galisot, G, Ramel, J-Y, Brouard, T, Tong, Q, Si, W, Liao, X, Zeng, G, Shi, Z, Zheng, G, Wang, C, MacGillivray, T, Newby, D, Rhode, K, Ourselin, S, Mohiaddin, R, Keegan, J, Firmin, D & Yang, G 2019, ' Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge ', Medical Image Analysis, vol. 58, pp. 101537 . https://doi.org/10.1016/j.media.2019.101537
Medical Image Analysis, Elsevier, 2019, 58, pp.101537. ⟨10.1016/j.media.2019.101537⟩
Zhuang, X, Li, L, Payer, C, Štern, D, Urschler, M, Heinrich, M P, Oster, J, Wang, C, Smedby, Ö, Bian, C, Yang, X, Heng, P-A, Mortazi, A, Bagci, U, Yang, G, Sun, C, Galisot, G, Ramel, J-Y, Brouard, T, Tong, Q, Si, W, Liao, X, Zeng, G, Shi, Z, Zheng, G, Wang, C, MacGillivray, T, Newby, D, Rhode, K, Ourselin, S, Mohiaddin, R, Keegan, J, Firmin, D & Yang, G 2019, ' Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge ', Medical Image Analysis, vol. 58, pp. 101537 . https://doi.org/10.1016/j.media.2019.101537
Highlights • This work presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. • This work introduc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::263c73bb410327bed3285b236ad28621
http://hdl.handle.net/10044/1/72782
http://hdl.handle.net/10044/1/72782
Publikováno v:
IEEE journal of biomedical and health informatics. 23(4)
Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this paper, we propose an automati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fa130b4959e53cbb79aa451d9cd31f9
https://pubmed.ncbi.nlm.nih.gov/31059459
https://pubmed.ncbi.nlm.nih.gov/31059459
Autor:
Darko Štern, Christian Payer, Savinien Bonheur, Martin Urschler, Horst Olschewski, Michael Pienn
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030322533
MICCAI (5)
MICCAI (5)
Despite some design limitations, CNNs have been largely adopted by the computer vision community due to their efficacy and versatility. Introduced by Sabour et al. to circumvent some limitations of CNNs, capsules replace scalars with vectors to encod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6eca724b0074742e82b9a8f4f4accf9c
https://doi.org/10.1007/978-3-030-32254-0_74
https://doi.org/10.1007/978-3-030-32254-0_74