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SegVol: Universal and Interactive Volumetric Medical Image Segmentation

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arxiv 2311.13385 v5 pith:5SKX7DTZ submitted 2023-11-22 cs.CV

Yuxin Du , Fan Bai , Tiejun Huang , Bo Zhao This is my paper
classification cs.CV
keywords segmentationimagemodelfoundationmedicalsegvolvolumetricanatomical
verification ladder T0 review T1 audit T2 compute T3 formal
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Precise image segmentation provides clinical study with instructive information. Despite the remarkable progress achieved in medical image segmentation, there is still an absence of a 3D foundation segmentation model that can segment a wide range of anatomical categories with easy user interaction. In this paper, we propose a 3D foundation segmentation model, named SegVol, supporting universal and interactive volumetric medical image segmentation. By scaling up training data to 90K unlabeled Computed Tomography (CT) volumes and 6K labeled CT volumes, this foundation model supports the segmentation of over 200 anatomical categories using semantic and spatial prompts. To facilitate efficient and precise inference on volumetric images, we design a zoom-out-zoom-in mechanism. Extensive experiments on 22 anatomical segmentation tasks verify that SegVol outperforms the competitors in 19 tasks, with improvements up to 37.24% compared to the runner-up methods. We demonstrate the effectiveness and importance of specific designs by ablation study. We expect this foundation model can promote the development of volumetric medical image analysis. The model and code are publicly available at: https://github.com/BAAI-DCAI/SegVol.

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