SAM-PR: enhancing 3D automated breast ultrasound imaging segmentation with probabilistic refinement of SAM

Ricardo Montoya-del-Angel; Marawan Elbatel; Joel Vidal; Robert Martí

doi:10.1117/12.3027023

29 May 2024 SAM-PR: enhancing 3D automated breast ultrasound imaging segmentation with probabilistic refinement of SAM

Ricardo Montoya-del-Angel, Marawan Elbatel, Joel Vidal, Robert Martí

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741P (2024) https://doi.org/10.1117/12.3027023
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

This work presents a framework for lesion segmentation on 3D Automated Breast Ultrasound. The method consists on the implementation of a state-of-the-art foundation model for 2D segmentation pipeline called Segment anything model (SAM), adapted for 3D segmentation through a probabilistic refinement technique. The presented method obtained second place in the segmentation task of the 2023 MICCAI Challenge on Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound (TDSC-ABUS 2023), being the most robust approach in terms of the Hausdorff distance. The paper describes the approaches developed for the challenge submission as well as suggestions for future improvement.

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

Citation Download Citation

Ricardo Montoya-del-Angel, Marawan Elbatel, Joel Vidal, and Robert Martí "SAM-PR: enhancing 3D automated breast ultrasound imaging segmentation with probabilistic refinement of SAM", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741P (29 May 2024); https://doi.org/10.1117/12.3027023

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