  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
