Refining boundaries of the segment anything model in medical images using an active contour model

Noor Nakhaei; Tengyue Zhang; Demetri Terzopoulos; William Hsu

doi:10.1117/12.3006255

3 April 2024 Refining boundaries of the segment anything model in medical images using an active contour model

Noor Nakhaei, Tengyue Zhang, Demetri Terzopoulos, William Hsu

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292734 (2024) https://doi.org/10.1117/12.3006255
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Accurate characterization of lesion boundaries is a critical task in diagnosing cancer. General large vision models (LVMs) such as the Segment Anything Model (SAM) have demonstrated promising performance in the semantic segmentation of natural images, but their test-time performance on medical images has been suboptimal, particularly in images with noise or poor contrast. To address this issue, we propose a test-time domain adaptation strategy that combines LVMs trained on large-scale datasets with the level-set active contours model, which can be tuned on a small subset of cases from the target dataset to yield more robust medical image segmentation performance at test-time. We showed the performance of our strategy on two datasets ISIC 2018 and CHASED DB1, which yielded a 0.3% and 0.11% improvement in segmentation accuracy, respectively, measured by Intersection over Union. We conclude that using active contours on top of LVMs for test-time domain adaptation can improve segmentation performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Noor Nakhaei, Tengyue Zhang, Demetri Terzopoulos, and William Hsu "Refining boundaries of the segment anything model in medical images using an active contour model", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292734 (3 April 2024); https://doi.org/10.1117/12.3006255

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