Research on Medical Image Segmentation Based on SAM and Its Future Prospects DOI Creative Commons
Kebin Fan, Liang Liang, Hao Li

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(6), P. 608 - 608

Published: June 3, 2025

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

Language: Английский

GlomSAM: Hybrid customized SAM for multi-glomerular detection and segmentation in immunofluorescence images DOI Creative Commons

Shengyu Pan,

Xuanli Tang,

Bingxian Chen

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0321096 - e0321096

Published: April 14, 2025

In nephrology research, multi-glomerular segmentation in immunofluorescence images plays a crucial role the early detection and diagnosis of chronic kidney disease. However, obtaining accurate segmentations often requires labor-intensive annotations existing methods are hampered by low recall rates limited accuracy. Recently, general Segment Anything Model (SAM) has demonstrated promising performance several tasks. this paper, SAM-based model (GlomSAM) is introduced to employ SAM pathology domain. The fusion encoder strategy utilizing advantages both convolution networks transformer structures with prompts conducted facilitate SAM’s transfer learning applications pathological analysis. Moreover, rough mask generator employed generate preliminary glomerular masks, enabling automated input prompting improving final results. Extensive comparative experiments ablation studies show its state-of-the-art surpassing other relevant research. We hope report will provide insights advance field promote more interesting work future.

Language: Английский

Citations

0
TP-SA3M: text prompts-assisted SAM for myopic maculopathy segmentation DOI

Tingyao Li,

Zehua Jiang,

Yixiao Jin

et al.

The Visual Computer, Journal Year: 2025, Volume and Issue: unknown

Published: May 3, 2025

Language: Английский

Citations

0
WeakPolyp-SAM: Segment Anything Model-driven weakly-supervised polyp segmentation DOI
Yiming Zhao, Tao Zhou,

Yunqi Gu

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113701 - 113701

Published: May 1, 2025

Language: Английский

Citations

0
A weakly supervised method for surgical scene components detection with visual foundation model DOI Creative Commons
Xiaoyan Zhang,

Jingyi Feng,

Qian Zhang

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0322751 - e0322751

Published: May 27, 2025

Purpose: Detection of crucial components is a fundamental problem in surgical scene understanding. Limited by the huge cost spatial annotation, current studies mainly focus on recognition three elements ⟨ instrument, verb, target id="M2">⟩ , while detection id="M3">⟨ id="M4">⟩ remains highly challenging. Some efforts have been made to detect components, yet their limitations include: (1) performance depends amount manual annotations; (2) No previous study has investigated targets. Methods: We introduce weakly supervised method for detecting key novelly combining triplet model and foundation Segment Anything Model (SAM). First, setting appropriate prompts, we used SAM generate candidate regions components. Then, preliminarily localize extracting positive activation areas class maps from model. However, using instrument’s as position attention guide leads positional deviations target’s resulting activation. To tackle this issue, propose RDV-AGC introducing an Attention Guide Correction (AGC) module. This module adjusts guidance according forward direction. Finally, match initial localization instruments targets with generated SAM, achieving precise scene. Results: Through ablation comparisons similar works, our achieved remarkable without requiring any annotations. Conclusion: introduced novel integrating visual

Language: Английский

Citations

0
Research on Medical Image Segmentation Based on SAM and Its Future Prospects DOI Creative Commons
Kebin Fan, Liang Liang, Hao Li

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(6), P. 608 - 608

Published: June 3, 2025

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

Language: Английский

Citations

0