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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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Pubblicato in:arXiv.org (Dec 20, 2024), p. n/a
Autore principale: Ma, Jun
Altri autori: Li, Feifei, Kim, Sumin, Asakereh, Reza, Bao-Hiep Le, Nguyen-Vu, Dang-Khoa, Pfefferle, Alexander, Muxin Wei, Gao, Ruochen, Lyu, Donghang, Yang, Songxiao, Purucker, Lennart, Marinov, Zdravko, Staring, Marius, Lu, Haisheng, Thuy Thanh Dao, Ye, Xincheng, Li, Zhi, Brugnara, Gianluca, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Cho, Jaeyoung, Mustafa Ahmed Mahmutoglu, Bendszus, Martin, Pflüger, Irada, Rastogi, Aditya, Ni, Dong, Yang, Xin, Zhou, Guang-Quan, Wang, Kaini, Heller, Nicholas, Papanikolopoulos, Nikolaos, Weight, Christopher, Tong, Yubing, Udupa, Jayaram K, Patrick, Cahill J, Wang, Yaqi, Zhang, Yifan, Contijoch, Francisco, McVeigh, Elliot, Ye, Xin, He, Shucheng, Haase, Robert, Pinetz, Thomas, Radbruch, Alexander, Krause, Inga, Kobler, Erich, He, Jian, Tang, Yucheng, Yang, Haichun, Huo, Yuankai, Luo, Gongning, Kaisar Kushibar, Amankulov, Jandos, Dias Toleshbayev, Mukhamejan, Amangeldi, Egger, Jan, Pepe, Antonio, Gsaxner, Christina, Luijten, Gijs, Fujita, Shohei, Kikuchi, Tomohiro, Wiestler, Benedikt, Kirschke, Jan S, de la Rosa, Ezequiel, Bolelli, Federico, Lumetti, Luca, Grana, Costantino, Xie, Kunpeng, Wu, Guomin, Puladi, Behrus, Martín-Isla, Carlos, Lekadir, Karim, Campello, Victor M, Shao, Wei, Brisbane, Wayne, Jiang, Hongxu, Hao, Wei, Wu, Yuan, Li, Shuangle, Zhou, Yuyin, Wang, Bo
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Algorithms
Source code
Reproducibility
Image segmentation
Open source software
Medical imaging
Accesso online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3148683611 
045 0 |b d20241220 
100 1 |a Ma, Jun 
245 1 |a Efficient MedSAMs: Segment Anything in Medical Images on Laptop 
260 |b Cornell University Library, arXiv.org  |c Dec 20, 2024 
513 |a Working Paper 
520 3 |a Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. 
653 |a Algorithms 
653 |a Source code 
653 |a Reproducibility 
653 |a Image segmentation 
653 |a Open source software 
653 |a Medical imaging 
700 1 |a Li, Feifei 
700 1 |a Kim, Sumin 
700 1 |a Asakereh, Reza 
700 1 |a Bao-Hiep Le 
700 1 |a Nguyen-Vu, Dang-Khoa 
700 1 |a Pfefferle, Alexander 
700 1 |a Muxin Wei 
700 1 |a Gao, Ruochen 
700 1 |a Lyu, Donghang 
700 1 |a Yang, Songxiao 
700 1 |a Purucker, Lennart 
700 1 |a Marinov, Zdravko 
700 1 |a Staring, Marius 
700 1 |a Lu, Haisheng 
700 1 |a Thuy Thanh Dao 
700 1 |a Ye, Xincheng 
700 1 |a Li, Zhi 
700 1 |a Brugnara, Gianluca 
700 1 |a Vollmuth, Philipp 
700 1 |a Foltyn-Dumitru, Martha 
700 1 |a Cho, Jaeyoung 
700 1 |a Mustafa Ahmed Mahmutoglu 
700 1 |a Bendszus, Martin 
700 1 |a Pflüger, Irada 
700 1 |a Rastogi, Aditya 
700 1 |a Ni, Dong 
700 1 |a Yang, Xin 
700 1 |a Zhou, Guang-Quan 
700 1 |a Wang, Kaini 
700 1 |a Heller, Nicholas 
700 1 |a Papanikolopoulos, Nikolaos 
700 1 |a Weight, Christopher 
700 1 |a Tong, Yubing 
700 1 |a Udupa, Jayaram K 
700 1 |a Patrick, Cahill J 
700 1 |a Wang, Yaqi 
700 1 |a Zhang, Yifan 
700 1 |a Contijoch, Francisco 
700 1 |a McVeigh, Elliot 
700 1 |a Ye, Xin 
700 1 |a He, Shucheng 
700 1 |a Haase, Robert 
700 1 |a Pinetz, Thomas 
700 1 |a Radbruch, Alexander 
700 1 |a Krause, Inga 
700 1 |a Kobler, Erich 
700 1 |a He, Jian 
700 1 |a Tang, Yucheng 
700 1 |a Yang, Haichun 
700 1 |a Huo, Yuankai 
700 1 |a Luo, Gongning 
700 1 |a Kaisar Kushibar 
700 1 |a Amankulov, Jandos 
700 1 |a Dias Toleshbayev 
700 1 |a Mukhamejan, Amangeldi 
700 1 |a Egger, Jan 
700 1 |a Pepe, Antonio 
700 1 |a Gsaxner, Christina 
700 1 |a Luijten, Gijs 
700 1 |a Fujita, Shohei 
700 1 |a Kikuchi, Tomohiro 
700 1 |a Wiestler, Benedikt 
700 1 |a Kirschke, Jan S 
700 1 |a de la Rosa, Ezequiel 
700 1 |a Bolelli, Federico 
700 1 |a Lumetti, Luca 
700 1 |a Grana, Costantino 
700 1 |a Xie, Kunpeng 
700 1 |a Wu, Guomin 
700 1 |a Puladi, Behrus 
700 1 |a Martín-Isla, Carlos 
700 1 |a Lekadir, Karim 
700 1 |a Campello, Victor M 
700 1 |a Shao, Wei 
700 1 |a Brisbane, Wayne 
700 1 |a Jiang, Hongxu 
700 1 |a Hao, Wei 
700 1 |a Wu, Yuan 
700 1 |a Li, Shuangle 
700 1 |a Zhou, Yuyin 
700 1 |a Wang, Bo 
773 0 |t arXiv.org  |g (Dec 20, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3148683611/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.16085