A fine-tuned foundational model SurgiSAM2 for surgical video anatomy segmentation and detection.

ABSTRACT: The foundational segmentation models, segmenting anything model (SAM) and SAM 2, have transformed segmentation by enabling remarkable zero-shot performance across diverse domains. In this study, we evaluate SAM 2 for surgical scene understanding by examining its semantic segmentation capabilities for organs/tissues both in zero-shot scenarios and after fine-tuning. We utilized five public datasets to evaluate and fine-tune SAM 2 for segmenting anatomical tissues in surgical videos/images. Fine-tuning was applied to the image encoder and mask decoder. We limited training subsets from 50 to 400 samples per class to better model real-world constraints with data acquisition. The impact of dataset size on fine-tuning performance was evaluated with weighted mean dice coefficient (WMDC), and the

SUBMITTER: Kamtam DN 

PROVIDER: S-EPMC12528661 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature