Liver segmentation: indications, techniques and future directions

Autor: Akshat Gotra, Lojan Sivakumaran, Gabriel Chartrand, Kim-Nhien Vu, Franck Vandenbroucke-Menu, Claude Kauffmann, Samuel Kadoury, Benoît Gallix, Jacques A. de Guise, An Tang

Publikováno v: Insights into Imaging, Vol 8, Iss 4, Pp 377-392 (2017)

Abstract Objectives Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to

Externí odkaz: https://doaj.org/article/2ca2a441ace74dad861107bcde28ed51

RADT-10. THE LOST METASTASES: DEEP LEARNING’S POTENTIAL IN RADIOSURGERY QUALITY ASSURANCE

Autor: Charmin Bang, Gabriel Chartrand, Sophie Pawlowski, Ramon Emiliani, Daniel Markel, Houda Bahig, Alexandre Samak, Selvan Rajakesari, Jérémi Lavoie, Simon Ducharme, David Roberge

Publikováno v: Neuro-Oncology. 24:vii50-vii51

Introduction Identifying, segmenting, measuring, and following multiple brain metastases treated with radiosurgery can be time consuming and error prone. Machine learning has shown promise for automated detection and segmentation. Recently, a U-Net i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9da3f888ffe49c95668251bf88e37431
https://doi.org/10.1093/neuonc/noac209.200

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

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes

Autor: An Tang, Kim-Nhien Vu, Miguel Chagnon, Guillaume Gilbert, Gabriel Chartrand, Marianne Chalut

Publikováno v: Journal of Magnetic Resonance Imaging. 43:1090-1099

Purpose To assess the agreement between published magnetic resonance imaging (MRI)-based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard. Materials and Methods This retrospectiv

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8748adc5aa738aab19cb4b31868562ea
https://doi.org/10.1002/jmri.25083

Liver segmentation: indications, techniques and future directions

Autor: Samuel Kadoury, Gabriel Chartrand, Akshat Gotra, Claude Kauffmann, Jacques A. de Guise, Lojan Sivakumaran, Franck Vandenbroucke-Menu, Kim-Nhien Vu, Benoit Gallix, An Tang

Publikováno v: Insights into Imaging
Insights into Imaging, Vol 8, Iss 4, Pp 377-392 (2017)

Objectives Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d9bfb162b5bcf9ae27d0ad9572c560b
https://pubmed.ncbi.nlm.nih.gov/28616760