Precise image segmentation provides clinical study with meaningful and
well-structured information. Despite the remarkable progress achieved in
medical image segmentation, there is still an absence of foundation
segmentation model that can segment a wide range of anatomical categories with
easy user interaction. In this paper, we propose a universal and interactive
volumetric medical image segmentation model, named SegVol. By training on 90k
unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation
model supports the segmentation of over 200 anatomical categories using
semantic and spatial prompts. Extensive experiments verify that SegVol
outperforms the state of the art by a large margin on multiple segmentation
benchmarks. Notably, on three challenging lesion datasets, our method achieves
around 20% higher Dice score than nnU-Net. The model and data are publicly
available at: this https URL

提出了一种名为 SegVol 的通用交互式体积医学图像分割模型，通过在 90k 个无标签的 CT 体积和 6k 个标记的 CT 上进行训练，该基础模型支持对 200 多个解剖类别的分割，使用语义和空间提示，在多个分割基准测试中表现明显优于现有技术，尤其在三个具有挑战性的病变数据集上，其 Dice 分数比 nnU-Net 高约 20%。