Segment anything model for medical images?

Authors:
raw_author_name: Yuhao Huang
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Xin Yang
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Lian Liu
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Han Zhou
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Ao Chang
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Xinrui Zhou
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Rusi Chen
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Junxuan Yu
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Jiongquan Chen
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Sijing Liu
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Haozhe Chi
raw_affiliation_string: Zhejiang University, Zhejiang, China
raw_author_name: Xindi Hu
raw_affiliation_string: Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, China
raw_author_name: Lei Li
raw_affiliation_string: Department of Engineering Science, University of Oxford, Oxford, UK
raw_author_name: Vicente Grau
raw_affiliation_string: Department of Engineering Science, University of Oxford, Oxford, UK
raw_author_name: Deng-Ping Fan
raw_affiliation_string: Computer Vision Lab (CVL), ETH Zurich, Zurich, Switzerland
raw_author_name: Fajin Dong
raw_affiliation_string: First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China; Ultrasound Department, the Second Clinical Medical College, Jinan University, China
raw_author_name: Dong Ni
raw_affiliation_string: Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China

Abstract: The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.