ORIGINAL RESEARCH article
Front. Med.
Sec. Pathology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1591999
This article is part of the Research TopicEvaluating Foundation Models in Medical ImagingView all 3 articles
Leveraging Advanced Feature Extraction for Improved Kidney Biopsy Segmentation
Provisionally accepted
Muhammad Wajeeh Us Sima1
Chengliang Wang1*Muhammad Arshad1Jamshed Ali Shaikh1
Salem Alkhalaf2*
Fahad Alturise3- 1College of Computer Science, Chongqing University, Chongqing, China
- 2Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia
- 3Department of Cybersecurity, College of Computer, Qassim University, Buraydah, Saudi Arabia
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Medical image segmentation faces critical challenges in renal histopathology due to the intricate morphology of glomeruli characterized by small size, fragmented structures, and low contrast against complex tissue backgrounds. While the Segment Anything Model (SAM) excels in natural image segmentation, its direct application to medical imaging underperforms due to (1) insufficient preservation of fine-grained anatomical details, (2) computational inefficiency on gigapixel whole-slide images (WSIs), and (3) poor adaptation to domain-specific features like staining variability and sparse annotations. To address these limitations, we propose V-SAM, a novel framework enhancing SAM's architecture through three key innovations: (1) a V-shaped adapter that preserves spatial hierarchies via multi-scale skip connections, recovering capillarylevel details lost in SAM's aggressive downsampling; (2) lightweight adapter layers that fine-tune SAM's frozen encoder with fewer trainable parameters, optimizing it for histopathology textures while avoiding catastrophic forgetting; and (3) a dynamic point-prompt mechanism enabling sub-pixel refinement of glomerular boundaries through gradient aware localization. Evaluated on the HuBMAP Hacking the Human Vasculature and Hacking the Kidney datasets, V-SAM achieves state-of-the-art performance, surpassing 89.31%, 97.65% accuracy, 86.17%, 95.54% F1-score respectively. V-SAM sets a new paradigm for adapting foundation models to clinical workflows, with direct applications in chronic kidney disease diagnosis and biomarker discovery. This work bridges the gap between SAM's generalizability and the precision demands of medical imaging, offering a scalable solution for resource constrained healthcare environments
Keywords: V-Sam, deep learning, Adapter Layer, Segment Anything Model, Point-Based Prompt
Received: 11 Mar 2025; Accepted: 13 May 2025.
Copyright: © 2025 Wajeeh Us Sima, Wang, Arshad, Shaikh, Alkhalaf and Alturise. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chengliang Wang, College of Computer Science, Chongqing University, Chongqing, 400044, China
Salem Alkhalaf, Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia
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