This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a natural image foundation model. The method proposed creates an initial coarse segmentation mask using the ALPnet prototypical network, augmented with a DINOv2 encoder. Following the extraction of an initial mask, prompts are extracted, such as points and bounding boxes, which are then input into the Segment Anything Model (SAM). State-of-the-art results are shown on several medical image datasets and demonstrate automated segmentation capabilities using a single image example (one shot) with no need for fine-tuning of the foundation model. Our code is available at: https://github.com/levayz/ProtoSAM
@article{arxiv.2407.07042,
title = {ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models},
author = {Lev Ayzenberg and Raja Giryes and Hayit Greenspan},
journal= {arXiv preprint arXiv:2407.07042},
year = {2024}
}