C
Christian Payer
Researcher at Graz University of Technology
Publications - 43
Citations - 1668
Christian Payer is an academic researcher from Graz University of Technology. The author has contributed to research in topics: Segmentation & Convolutional neural network. The author has an hindex of 16, co-authored 40 publications receiving 973 citations. Previous affiliations of Christian Payer include University of Auckland.
Papers
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Book ChapterDOI
Regressing Heatmaps for Multiple Landmark Localization Using CNNs
TL;DR: Evaluation of different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.
Journal ArticleDOI
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Xiahai Zhuang,Lei Li,Christian Payer,Darko Štern,Martin Urschler,Mattias P. Heinrich,Julien Oster,Chunliang Wang,Örjan Smedby,Cheng Bian,Xin Yang,Pheng-Ann Heng,Aliasghar Mortazi,Ulas Bagci,Guanyu Yang,Chenchen Sun,Gaetan Galisot,Jean-Yves Ramel,Thierry Brouard,Qianqian Tong,Weixin Si,Xiangyun Liao,Guodong Zeng,Zenglin Shi,Guoyan Zheng,Chengjia Wang,Tom MacGillivray,David E. Newby,Kawal Rhode,Sebastien Ourselin,Raad Mohiaddin,Jennifer Keegan,David N. Firmin,Guang Yang +33 more
TL;DR: 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.
Journal ArticleDOI
Integrating spatial configuration into heatmap regression based CNNs for landmark localization.
TL;DR: This work proposes a CNN architecture that learns to split the localization task into two simpler sub‐problems, reducing the overall need for large training datasets, and proposes a fully convolutional SpatialConfiguration‐Net (SCN), which outperforms related methods in terms of landmark localization error.
Book ChapterDOI
Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations
TL;DR: Results on the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge show that the proposed architecture performs well on the provided CT and MRI training volumes, delivering in a three-fold cross validation an average Dice Similarity Coefficient over all heart substructures.
Journal ArticleDOI
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina,Malek El Husseini,Amirhossein Bayat,Maximilian T. Löffler,Hans Liebl,Hongwei Li,Giles Tetteh,Jan Kukačka,Christian Payer,Darko Štern,Martin Urschler,Maodong Chen,Dalong Cheng,Nikolas Lessmann,Yujin Hu,Tianfu Wang,Dong Yang,Daguang Xu,Felix Ambellan,Tamaz Amiranashvili,Moritz Ehlke,Hans Lamecker,Sebastian Lehnert,Marilia Lirio,Nicolás Pérez de Olaguer,Heiko Ramm,Manish Sahu,Alexander Tack,Stefan Zachow,Tao Jiang,Xinjun Ma,Christoph Angerman,Xin Wang,Kevin W. Brown,Alexandre Kirszenberg,Elodie Puybareau,Di Chen,Yiwei Bai,Brandon H. Rapazzo,Timyoas Yeah,Amber Zhang,Shangliang Xu,Feng Hou,Zhiqiang He,Chan Zeng,Zheng Xiangshang,Xu Liming,Tucker Netherton,Raymond P. Mumme,Laurence E. Court,Zixun Huang,Chenhang He,Li-Wen Wang,Sai Ho Ling,Lê Duy Huỳnh,Nicolas Boutry,Roman Jakubicek,Jiri Chmelik,Supriti Mulay,Mohanasankar Sivaprakasam,Johannes C. Paetzold,Suprosanna Shit,Ivan Ezhov,Benedikt Wiestler,Ben Glocker,Alexander Valentinitsch,Markus Rempfler,Björn H. Menze,Jan S. Kirschke +68 more
TL;DR: The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations.