  Precise image segmentation provides clinical study with instructive
information. Despite the remarkable progress achieved in medical image
segmentation, there is still an absence of 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. Extensive experiments on 10 internal validation tasks and 18 external
validation tasks verify that SegVol outperforms the state of the art by a large
margin. Through its capacity to provide precise volumetric segmentation across
various anatomical categories, SegVol has the potential to accelerate
advancements in medical imaging diagnosis and facilitate treatment
optimization. The model and code are publicly available at:
https://github.com/BAAI-DCAI/SegVol.
