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2024

ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

Ayzenberg, Lev, Giryes, Raja, Greenspan, Hayit

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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).

ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models · Around

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