One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios A. Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand and 99 more
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 Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Bilic, P., Christ, P. F., Li, H., Vorontsov, E., Ben-Cohen, A., Kaissis, G. A., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., Lohöfer, F. K., Holch, J. W., Sommer, W. H., Hofmann, F., Hostettler, A., Lev‐Cohain, N., Drozdzal, M., Amitai, M., Vivanti, R., … Menze, B. H. (2022). The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 84, 102680. https://doi.org/10.1016/j.media.2022.102680
Bilic, Patrick, et al. "The Liver Tumor Segmentation Benchmark (LiTS)." Medical Image Analysis, vol. 84, 2022, pp. 102680, https://doi.org/10.1016/j.media.2022.102680.
Bilic, Patrick, Patrick Ferdinand Christ, Hongwei Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios A. Kaissis, Adi Szeskin, et al. "The Liver Tumor Segmentation Benchmark (LiTS)." Medical Image Analysis 84 (2022): 102680. https://doi.org/10.1016/j.media.2022.102680.
Bilic P., Christ P. F., Li H. [и др.] The Liver Tumor Segmentation Benchmark (LiTS) // Medical Image Analysis. — 2022. — Vol. 84. — P. 102680. — URL: https://doi.org/10.1016/j.media.2022.102680
@article{bilic2022liver,
title = {{The Liver Tumor Segmentation Benchmark (LiTS)}},
author = {Patrick Bilic and Patrick Ferdinand Christ and Hongwei Li and Eugene Vorontsov and Avi Ben-Cohen and Georgios A. Kaissis and Adi Szeskin and Colin Jacobs and Gabriel Efrain Humpire Mamani and Gabriel Chartrand and Fabian Karl Lohöfer and Julian Walter Holch and Wieland H. Sommer and Felix Hofmann and Alexandre Hostettler and Naama Lev‐Cohain and Michal Drozdzal and Michal Amitai and Refael Vivanti and Jacob Sosna and Ivan Ezhov and Anjany Sekuboyina and Fernando Navarro and Florian Kofler and Johannes Christian Paetzold and Suprosanna Shit and Xiaobin Hu and Jana Lipková and Markus Rempfler and Marie Piraud and Jan Stefan Kirschke and Benedikt Wiestler and Zhiheng Zhang and Christian Hülsemeyer and Marcel Beetz and Florian Ettlinger and Michela Antonelli and Woong Bae and Míriam Bellver and Lei Bi and Hao Chen and Grzegorz Chlebus and Erik B. Dam and Qi Dou and Chi‐Wing Fu and Bogdan Georgescu and Xavier Giró-i-Nieto and Felix Gruen and Xu Han and Pheng‐Ann Heng and Jürgen Hesser and Jan Hendrik Moltz and Christian Igel and Fabian Isensee and Paul F. Jäger and Fucang Jia and Krishna Chaitanya Kaluva and Mahendra Khened and Ildoo Kim and Jae Hun Kim and Sungwoong Kim and Simon Köhl and Tomasz Konopczyński and Avinash Kori and Ganapathy Krishnamurthi and Fan Li and Hongchao Li and Junbo Li and Xiaomeng Li and John S. Lowengrub and Jun Ma and Klaus Hermann Maier-Hein and Kevis-Kokitsi Maninis and Hans Meine and Dorit Merhof and Akshay Pai and Mathias Perslev and Jens Petersen and Jordi Pont-Tuset and Qi Jin and Xiaojuan Qi and Oliver Rippel and Karsten Roth and Ignacio Sarasúa and Andrea Schenk and Zengming Shen and Jordi Torres and Christian Wachinger and Chunliang Wang and Leon Weninger and Jianrong Wu and Daguang Xu and Xiaoping Yang and Simon C.H. Yu and Yading Yuan and Yue Miao and Liping Zhang and M. Jorge Cardoso and Spyridon Bakas and Rickmer Braren and Volker Heinemann and Christopher Pal and An Tang and Samuel Kadoury and Luc Soler and Bram van Ginneken and Hayit K. Greenspan and Leo Joskowicz B and Bjoern H. Menze},
journal = {Medical Image Analysis},
year = {2022},
volume = {84},
pages = {102680},
doi = {10.1016/j.media.2022.102680},
url = {https://doi.org/10.1016/j.media.2022.102680}
}TY - JOUR AU - Bilic, Patrick AU - Christ, Patrick Ferdinand AU - Li, Hongwei AU - Vorontsov, Eugene AU - Ben-Cohen, Avi AU - Kaissis, Georgios A. AU - Szeskin, Adi AU - Jacobs, Colin AU - Mamani, Gabriel Efrain Humpire AU - Chartrand, Gabriel AU - Lohöfer, Fabian Karl AU - Holch, Julian Walter AU - Sommer, Wieland H. AU - Hofmann, Felix AU - Hostettler, Alexandre AU - Lev‐Cohain, Naama AU - Drozdzal, Michal AU - Amitai, Michal AU - Vivanti, Refael AU - Sosna, Jacob AU - Ezhov, Ivan AU - Sekuboyina, Anjany AU - Navarro, Fernando AU - Kofler, Florian AU - Paetzold, Johannes Christian AU - Shit, Suprosanna AU - Hu, Xiaobin AU - Lipková, Jana AU - Rempfler, Markus AU - Piraud, Marie AU - Kirschke, Jan Stefan AU - Wiestler, Benedikt AU - Zhang, Zhiheng AU - Hülsemeyer, Christian AU - Beetz, Marcel AU - Ettlinger, Florian AU - Antonelli, Michela AU - Bae, Woong AU - Bellver, Míriam AU - Bi, Lei AU - Chen, Hao AU - Chlebus, Grzegorz AU - Dam, Erik B. AU - Dou, Qi AU - Fu, Chi‐Wing AU - Georgescu, Bogdan AU - Giró-i-Nieto, Xavier AU - Gruen, Felix AU - Han, Xu AU - Heng, Pheng‐Ann AU - Hesser, Jürgen AU - Moltz, Jan Hendrik AU - Igel, Christian AU - Isensee, Fabian AU - Jäger, Paul F. AU - Jia, Fucang AU - Kaluva, Krishna Chaitanya AU - Khened, Mahendra AU - Kim, Ildoo AU - Kim, Jae Hun AU - Kim, Sungwoong AU - Köhl, Simon AU - Konopczyński, Tomasz AU - Kori, Avinash AU - Krishnamurthi, Ganapathy AU - Li, Fan AU - Li, Hongchao AU - Li, Junbo AU - Li, Xiaomeng AU - Lowengrub, John S. AU - Ma, Jun AU - Maier-Hein, Klaus Hermann AU - Maninis, Kevis-Kokitsi AU - Meine, Hans AU - Merhof, Dorit AU - Pai, Akshay AU - Perslev, Mathias AU - Petersen, Jens AU - Pont-Tuset, Jordi AU - Jin, Qi AU - Qi, Xiaojuan AU - Rippel, Oliver AU - Roth, Karsten AU - Sarasúa, Ignacio AU - Schenk, Andrea AU - Shen, Zengming AU - Torres, Jordi AU - Wachinger, Christian AU - Wang, Chunliang AU - Weninger, Leon AU - Wu, Jianrong AU - Xu, Daguang AU - Yang, Xiaoping AU - Yu, Simon C.H. AU - Yuan, Yading AU - Miao, Yue AU - Zhang, Liping AU - Cardoso, M. Jorge AU - Bakas, Spyridon AU - Braren, Rickmer AU - Heinemann, Volker AU - Pal, Christopher AU - Tang, An AU - Kadoury, Samuel AU - Soler, Luc AU - Ginneken, Bram van AU - Greenspan, Hayit K. AU - B, Leo Joskowicz AU - Menze, Bjoern H. TI - The Liver Tumor Segmentation Benchmark (LiTS) JO - Medical Image Analysis VL - 84 SP - 102680 PY - 2022 DA - 2022/11/17 DO - 10.1016/j.media.2022.102680 UR - https://doi.org/10.1016/j.media.2022.102680 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.