ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
<|reference_start|>ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models: 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<|reference_end|>
@article{ayzenberg2024protosam:,
    title={ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models},
    author={Lev Ayzenberg, Raja Giryes, Hayit Greenspan},
    journal={arXiv preprint arXiv:2407.07042},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07042},
    primaryClass={cs.CV cs.AI}
}