MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation

• URL: http://arxiv.org/abs/2403.14103v1
• Date: Thu, 21 Mar 2024 03:28:24 GMT
• Title: MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation
• Authors: Bin Xie, Hao Tang, Bin Duan, Dawen Cai, Yan Yan,
• Abstract summary: MaskSAM is a mask classification prompt-free adaptation framework for medical image segmentation. Our method achieves state-of-the-art performance on AMOS2022, 90.52% Dice, which improved by 2.7% compared to nnUNet.
• Score: 18.125292369318576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segment Anything Model~(SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance. However, SAM does not work when directly applied to medical image segmentation tasks, since SAM lacks the functionality to predict semantic labels for predicted masks and needs to provide extra prompts, such as points or boxes, to segment target regions. Meanwhile, there is a huge gap between 2D natural images and 3D medical images, so the performance of SAM is imperfect for medical image segmentation tasks. Following the above issues, we propose MaskSAM, a novel mask classification prompt-free SAM adaptation framework for medical image segmentation. We design a prompt generator combined with the image encoder in SAM to generate a set of auxiliary classifier tokens, auxiliary binary masks, and auxiliary bounding boxes. Each pair of auxiliary mask and box prompts, which can solve the requirements of extra prompts, is associated with class label predictions by the sum of the auxiliary classifier token and the learnable global classifier tokens in the mask decoder of SAM to solve the predictions of semantic labels. Meanwhile, we design a 3D depth-convolution adapter for image embeddings and a 3D depth-MLP adapter for prompt embeddings. We inject one of them into each transformer block in the image encoder and mask decoder to enable pre-trained 2D SAM models to extract 3D information and adapt to 3D medical images. Our method achieves state-of-the-art performance on AMOS2022, 90.52% Dice, which improved by 2.7% compared to nnUNet. Our method surpasses nnUNet by 1.7% on ACDC and 1.0% on Synapse datasets.

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