A review of the Segment Anything Model (SAM) for medical image analysis: Accomplishments and perspectives.

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Mudassar Ali, Tong Wu, Haoji Hu, Qiong Luo, Dong Xu, Weizeng Zheng, Neng Jin, Chen Yang, Jincao Yao
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引用次数: 0

Abstract

The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model (SAM) within the medical image segmentation category over the course of the past year. However, SAM has demonstrated notable achievements in adapting to medical image segmentation tasks through fine-tuning on medical datasets, transitioning from 2D to 3D datasets, and optimizing prompting engineering. This is despite the fact that direct application on medical datasets has shown mixed results. Despite the difficulties, the paper emphasizes the significant potential that SAM possesses in the field of medical segmentation. One of the suggested directions for the future is to investigate the construction of large-scale datasets, to address multi-modal and multi-scale information, to integrate with semi-supervised learning structures, and to extend the application methods of SAM in clinical settings. In addition to making a significant contribution to the field of medical segmentation.

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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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