Research on Medical Image Segmentation Based on SAM and Its Future Prospects
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/6/608 |
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| author | Kangxu Fan Liang Liang Hao Li Weijun Situ Wei Zhao Ge Li |
| author_facet | Kangxu Fan Liang Liang Hao Li Weijun Situ Wei Zhao Ge Li |
| author_sort | Kangxu Fan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-77be122f20b84b7fbbbd1d4d75139024 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-77be122f20b84b7fbbbd1d4d751390242025-08-20T03:26:57ZengMDPI AGBioengineering2306-53542025-06-0112660810.3390/bioengineering12060608Research on Medical Image Segmentation Based on SAM and Its Future ProspectsKangxu Fan0Liang Liang1Hao Li2Weijun Situ3Wei Zhao4Ge Li5School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaDepartment of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, ChinaDepartment of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, ChinaDepartment of Radiology, Xiangya Hospital, Central South University, Changsha 410008, ChinaThe 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.https://www.mdpi.com/2306-5354/12/6/608Segment Anything Modelmedical image segmentation |
| spellingShingle | Kangxu Fan Liang Liang Hao Li Weijun Situ Wei Zhao Ge Li Research on Medical Image Segmentation Based on SAM and Its Future Prospects Bioengineering Segment Anything Model medical image segmentation |
| title | Research on Medical Image Segmentation Based on SAM and Its Future Prospects |
| title_full | Research on Medical Image Segmentation Based on SAM and Its Future Prospects |
| title_fullStr | Research on Medical Image Segmentation Based on SAM and Its Future Prospects |
| title_full_unstemmed | Research on Medical Image Segmentation Based on SAM and Its Future Prospects |
| title_short | Research on Medical Image Segmentation Based on SAM and Its Future Prospects |
| title_sort | research on medical image segmentation based on sam and its future prospects |
| topic | Segment Anything Model medical image segmentation |
| url | https://www.mdpi.com/2306-5354/12/6/608 |
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