F
Felix Hofmann
Researcher at Ludwig Maximilian University of Munich
Publications - 13
Citations - 1495
Felix Hofmann is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Medicine & Segmentation. The author has an hindex of 5, co-authored 10 publications receiving 954 citations.
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The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic,Patrick Ferdinand Christ,Eugene Vorontsov,Grzegorz Chlebus,Hao Chen,Qi Dou,Chi-Wing Fu,Xiao Han,Pheng-Ann Heng,Jürgen Hesser,Samuel Kadoury,Tomasz Konopczynski,Miao Le,Chunming Li,Xiaomeng Li,Jana Lipkova,John Lowengrub,Hans Meine,Jan Hendrik Moltz,Chris Pal,Marie Piraud,Xiaojuan Qi,Jin Qi,Markus Rempfler,Karsten Roth,Andrea Schenk,Anjany Sekuboyina,Ping Zhou,Christian Hülsemeyer,Marcel Beetz,Florian Ettlinger,Felix Gruen,Georgios Kaissis,Fabian Lohöfer,Rickmer Braren,Julian Walter Holch,Felix Hofmann,Wieland H. Sommer,Volker Heinemann,Colin Jacobs,Gabriel Efrain Humpire Mamani,Bram van Ginneken,Gabriel Chartrand,An Tang,Michal Drozdzal,Avi Ben-Cohen,Eyal Klang,Marianne M. Amitai,Eli Konen,Hayit Greenspan,Johan Moreau,Alexandre Hostettler,Luc Soler,Refael Vivanti,Adi Szeskin,Naama Lev-Cohain,Jacob Sosna,Leo Joskowicz,Bjoern H. Menze +58 more
TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.
Book ChapterDOI
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ,Mohamed Ezzeldin A. Elshaer,Florian Ettlinger,Sunil Tatavarty,Marc Bickel,Patrick Bilic,Markus Rempfler,Marco Armbruster,Felix Hofmann,Melvin D'Anastasi,Wieland H. Sommer,Seyed-Ahmad Ahmadi,Bjoern H. Menze +12 more
TL;DR: In this paper, a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs) is presented.
Posted Content
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks.
Patrick Ferdinand Christ,Florian Ettlinger,Felix Grün,Mohamed Ezzeldin A. Elshaera,Jana Lipkova,Sebastian Schlecht,Freba Ahmaddy,Sunil Tatavarty,Marc Bickel,Patrick Bilic,Markus Rempfler,Felix Hofmann,Melvin D’ Anastasi,Seyed-Ahmad Ahmadi,Georgios Kaissis,Julian Walter Holch,Wieland H. Sommer,Rickmer Braren,Volker Heinemann,Bjoern H. Menze +19 more
TL;DR: Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume.
Book ChapterDOI
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ,Mohamed Ezzeldin A. Elshaer,Florian Ettlinger,Sunil Tatavarty,Marc Bickel,Patrick Bilic,Markus Rempfler,Marco Armbruster,Felix Hofmann,Melvin D'Anastasi,Wieland H. Sommer,Seyed-Ahmad Ahmadi,Bjoern H. Menze +12 more
TL;DR: The results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over \(94\,\%\) for liver with computation times below 100 s per volume.
Journal ArticleDOI
CT attenuation of liver metastases before targeted therapy is a prognostic factor of overall survival in colorectal cancer patients. Results from the randomised, open-label FIRE-3/AIO KRK0306 trial
Matthias F Froelich,Volker Heinemann,Volker Heinemann,Wieland H. Sommer,Julian Walter Holch,Julian Walter Holch,Franziska Schoeppe,Nina Hesse,Alena B. Baumann,Wolfgang G. Kunz,Maximilian F. Reiser,Jens Ricke,Melvin D'Anastasi,Sebastian Stintzing,Sebastian Stintzing,Dominik Paul Modest,Dominik Paul Modest,Philipp M. Kazmierczak,Felix Hofmann +18 more
TL;DR: Increased mean baseline CT attenuation of liver metastases in baseline computed tomography is a prognostic factor for prolonged OS in colorectal cancer patients and may identify mCRC patients with prolonged OS and better tumour response.