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

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.