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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The rapid advancement of prompt-based models in natural language processing and image generation has revolutionized the field of image segmentation. The introduction of the Segment Anything Model (SAM) has further invigorated this domain with its unprecedented versatility. However, its applicability to medical image segmentation remains uncertain due to significant disparities between natural and medical images, which demand careful consideration. This study comprehensively analyzes recent efforts to adapt SAM for medical image segmentation, including empirical benchmarking and methodological refinements aimed at bridging the gap between SAM’s capabilities and the unique challenges of medical imaging. Furthermore, we explore future directions for SAM in this field. While direct application of SAM to complex, multimodal, and multi-target medical datasets may not yet yield optimal results, insights from these efforts provide crucial guidance for developing foundational models tailored to the intricacies of medical image analysis. Despite existing challenges, SAM holds considerable potential to demonstrate its unique advantages and robust capabilities in medical image segmentation in the near future.

Details

Title
Research on Medical Image Segmentation Based on SAM and Its Future Prospects
Author
Fan Kangxu 1 ; Liang, Liang 1 ; Li, Hao 1 ; Situ Weijun 2 ; Zhao, Wei 2 ; Li, Ge 3 

 School of Computer Science and Engineering, Central South University, Changsha 410083, China 
 Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China 
 Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China 
First page
608
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
23065354
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3223876742
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.