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Recently, large vision model, Segment Anything Model (SAM), has revolutionized the computer vision field, especially for image segmentation.
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig · 2006
Earlier work this paper cites.
3D slicer as an image computing platform for the quantitative imaging network
Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy-Cramer, Julien Finet, Jean-Christophe Fillion-Robin, Sonia Pujol, Christian Bauer, Dominique Jennings, Fiona Fennessy, Milan Sonka, et al · 2012
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Evaluation of six registration methods for the human abdomen on clinically acquired ct
Zhoubing Xu, Christopher P Lee, Mattias P Heinrich, Marc Modat, Daniel Rueckert, Sebastien Ourselin, Richard G Abramson, and Bennett A Landman · 2016
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Spinal cord grey matter segmentation challenge
Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso, Benjamin N Conrad, Esha Datta, Gergely Dávid, Benjamin De Leener, et al · 2017
Earlier work this paper cites.
PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images
Samuel G. Armato, Henkjan Huisman, Karen Drukker, Lubomir Hadjiiski, Justin S. Kirby, Nicholas Petrick, George Redmond, Maryellen L. Giger, Kenny Cha, Artem Mamonov, Jayashree Kalpathy-Cramer, and Keyvan Farahani · 2018
Earlier work this paper cites.
PANet: Few-shot image semantic segmentation with prototype alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng · 2019
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
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‘Squeeze & excite’ guided few-shot segmentation of volumetric images
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger · 2020
Earlier work this paper cites.
CT-ORG, a new dataset for multiple organ segmentation in computed tomography
Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin · 2020
Earlier work this paper cites.
Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning
Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, and Shaoting Zhang · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Going to extremes: Weakly supervised medical image segmentation
Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, and Daguang Xu · 2021
Cited alongside, same era.
Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification
Simon Graham, Mostafa Jahanifar, Ayesha Azam, Mohammed Nimir, Yee-Wah Tsang, Katherine Dodd, Emily Hero, Harvir Sahota, Atisha Tank, Ksenija Benes, et al · 2021
Cited alongside, same era.
Reviving iterative training with mask guidance for interactive segmentation
Konstantin Sofiiuk, Ilya A Petrov, and Anton Konushin · 2022
Cited alongside, same era.
Simpleclick: Interactive image segmentation with simple vision transformers
Qin Liu, Zhenlin Xu, Gedas Bertasius, and Marc Niethammer · 2022
Cited alongside, same era.
Florian Putz, Johanna Grigo, Thomas Weissmann, Philipp Schubert, Daniel Hoefler, Ahmed Gomaa, Hassen Ben Tkhayat, Amr Hagag, Sebastian Lettmaier, Benjamin Frey, et al · 2023
Closest in time.
Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, and Chen Gong · 2023
Closest in time.
Polyp-sam: Transfer sam for polyp segmentation
Yuheng Li, Mingzhe Hu, and Xiaofeng Yang · 2023
Closest in time.
Zhongxi Qiu, Yan Hu, Heng Li, and Jiang Liu · 2023
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Segment anything model for medical image analysis: an experimental study, 2023
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Deepedit: Deep editable learning for interactive segmentation of 3d medical images
Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle, Muhammad Asad, Richard Brown, Vishwesh Nath, Alvin Ihsani, Michela Antonelli, Daniel Palkovics, Csaba Pinter, Ron Alkalay, Steve Pieper, Holger R. Roth, Daguang Xu, Prerna Dogra, Tom Vercauteren, Andrew Feng, Abood Quraini, Sebastien Ourselin, and M. Jorge Cardoso · 2022
Cited alongside, same era.
CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data
Yuxin Song, Jing Zheng, Long Lei, Zhipeng Ni, Baoliang Zhao, and Ying Hu · 2022
Cited alongside, same era.
The medical segmentation decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M Summers, et al · 2022
Cited alongside, same era.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
Cited alongside, same era.
Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas W Remedios, Shunxing Bao, Bennett A Landman, Lee E Wheless, Lori A Coburn, Keith T Wilson, et al · 2023
Cited alongside, same era.
Chuanfei Hu and Xinde Li · 2023
Cited alongside, same era.
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model
Tassilo Wald, Saikat Roy, Gregor Koehler, Nico Disch, Maximilian Rouven Rokuss, Julius Holzschuh, David Zimmerer, and Klaus Maier-Hein · 2023
Cited alongside, same era.
Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang · 2023
Closest in time.
Sam on medical images: A comprehensive study on three prompt modes, 2023
Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang, Qicheng Lao, and Kang Li · 2023
Closest in time.
Segment anything in medical images, 2023
Jun Ma and Bo Wang · 2023
Closest in time.
Orthogonal annotation benefits barely-supervised medical image segmentation
Heng Cai, Shumeng Li, Lei Qi, Qian Yu, Yinghuan Shi, and Yang Gao · 2023
Closest in time.
Annotating 8,000 abdominal ct volumes for multi-organ segmentation in three weeks, 2023
Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, Alan Yuille, and Zongwei Zhou · 2023
Closest in time.
Medical sam adapter: Adapting segment anything model for medical image segmentation
Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhaowei Wang, Yanwu Xu, Yueming Jin, and Tal Arbel · 2023
Closest in time.
Sam-med2d, 2023
Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Junjun He, Shaoting Zhang, Min Zhu, and Yu Qiao · 2023
Closest in time.
The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Christ, et al · 2023
Closest in time.