DOI: 10.18413/2518-1092-2025-10-3-0-5

ON SEGMENTATION OF POLYPS USING SEGMENT ANYTHING MODEL

Eugene Yurievich Shchetinin
Anastasiia Alexandrovna Tiutiunnik

Colorectal polyps are critical precursors to colorectal cancer, and their early detection is vital for effective prevention. This study introduces Polyps-SAM2 – an enhanced polyp segmentation model built upon the Segment Anything Model 2 (SAM2) foundation. Tailored for medical imaging, Polyps-SAM2 incorporates fine-tuning with a frozen image encoder and integrates trainable layers for processing textual prompts. Evaluated on two benchmark datasets – Kvasir-Seg and CVC-ClinicDB – the model achieves Dice and IoU scores of 0.94/0.91 and 0.938/0.901, respectively, outperforming or matching state-of-the-art segmentation approaches. While limitations remain – particularly in handling images with multiple distinct polyps and reliance on user-provided prompts such as bounding boxes – the model demonstrates strong generalization capabilities and significant potential for clinical deployment in computer-aided colonoscopy systems, thereby improving diagnostic accuracy and workflow efficiency for physicians.

Keywords: colon polyps, segmentation, deep learning, fundamental computer vision models, transformer.

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Shchetinin E.Yu., Tiutiunnik A.A. On Segmentation of Polyps Using Segment Anything Model // Research result. Information technologies. – Т.10, №3, 2025. – P. 55-63. DOI: 10.18413/2518-1092-2025-10-3-0-5

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