The Liver Tumor Segmentation Benchmark (LiTS)

Autor: Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yue, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

Publikováno v: Med. Image Anal. 84:102680 (2022)
Medical Image Analysis, 84
Bilic, P, Christ, P, Li, H B, Vorontsov, E, Ben-Cohen, A, Kaissis, G, Szeskin, A, Jacobs, C, Mamani, G E H, Chartrand, G, Lohöfer, F, Holch, J W, Sommer, W, Hofmann, F, Hostettler, A, Lev-Cohain, N, Drozdzal, M, Amitai, M M, Vivanti, R, Sosna, J, Ezhov, I, Sekuboyina, A, Navarro, F, Kofler, F, Paetzold, J C, Shit, S, Hu, X, Lipková, J, Rempfler, M, Piraud, M, Kirschke, J, Wiestler, B, Zhang, Z, Hülsemeyer, C, Beetz, M, Ettlinger, F, Antonelli, M, Bae, W, Bellver, M, Bi, L, Chen, H, Chlebus, G, Dam, E B, Dou, Q, Fu, C-W, Georgescu, B, Giró-I-Nieto, X, Gruen, F, Han, X, Heng, P-A, Hesser, J, Moltz, J H, Igel, C, Isensee, F, Jäger, P, Jia, F, Kaluva, K C, Khened, M, Kim, I, Kim, J-H, Kim, S, Kohl, S, Konopczynski, T, Kori, A, Krishnamurthi, G, Li, F, Li, H, Li, J, Li, X, Lowengrub, J, Ma, J, Maier-Hein, K, Maninis, K-K, Meine, H, Merhof, D, Pai, A, Perslev, M, Petersen, J, Pont-Tuset, J, Qi, J, Qi, X, Rippel, O, Roth, K, Sarasua, I, Schenk, A, Shen, Z, Torres, J, Wachinger, C, Wang, C, Weninger, L, Wu, J, Xu, D, Yang, X, Yu, S C-H, Yuan, Y, Yue, M, Zhang, L, Cardoso, J, Bakas, S, Braren, R, Heinemann, V, Pal, C, Tang, A, Kadoury, S, Soler, L, van Ginneken, B, Greenspan, H, Joskowicz, L & Menze, B 2023, ' The Liver Tumor Segmentation Benchmark (LiTS) ', Medical Image Analysis, vol. 84, 102680 . https://doi.org/10.1016/j.media.2022.102680

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical I

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e22b7afec9cac0b49d2cb615d2787280
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=67003

Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection

Autor: Hayit Greenspan, Stephen P. Raskin, Avi Ben-Cohen, Simona Ben-Haim, Eyal Klang, Shelly Soffer, Michal Amitai, Eli Konen

Publikováno v: Engineering Applications of Artificial Intelligence. 78:186-194

In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::93fce5f5197be0ed199709e58d609831
https://doi.org/10.1016/j.engappai.2018.11.013

Liver lesion detection in CT using deep learning techniques

Autor: Hayit Greenspan, Avi Ben-Cohen

Publikováno v: Handbook of Medical Image Computing and Computer Assisted Intervention ISBN: 9780128161760

Computer aided diagnosis (CAD) tools have the potential to support the radiologists in detection and can lead to improved diagnosis. This chapter presents deep learning based techniques for automated liver lesion analysis in computed tomography (CT)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a7ba6d01e08bbab3edb2ced08de23b88
https://doi.org/10.1016/b978-0-12-816176-0.00008-9

Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations

Autor: Eli Konen, Ariel Kerpel, Avi Ben-Cohen, Hayit Greenspan, Michal Amitai, Eyal Klang

Publikováno v: Neurocomputing. 275:1585-1594

In this work we focus on liver metastases detection in computed tomography (CT) examinations, using both a global context with a fully convolutional network (FCN), and a local patch level analysis with superpixel sparse based classification. The task

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dd4a9d67ed460314b6687c196c07a1ee
https://doi.org/10.1016/j.neucom.2017.10.001

Anatomical Data Augmentation For CNN based Pixel-wise Classification

Autor: Eyal Klang, Jacob Goldberger, Avi Ben-Cohen, Hayit Greenspan, Michal Amitai

Publikováno v: ISBI

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended la

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94b0b9335ebc7e1b960dbc293c6ea934