EchoONE, a SAM-based model with prototype-composed dense prompts and a local-feature fusion branch, segments multiple echocardiography planes in one model and reports state-of-the-art Dice scores on internal and external datasets.
PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation
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abstract
The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.
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EchoONE: Segmenting Multiple echocardiography Planes in One Model
EchoONE, a SAM-based model with prototype-composed dense prompts and a local-feature fusion branch, segments multiple echocardiography planes in one model and reports state-of-the-art Dice scores on internal and external datasets.