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: https://github.com/BAAI-DCAI/SegVol.

翻译：摘要：精确的图像分割为临床研究提供了有意义且结构化的信息。尽管医学图像分割取得了显著进展，但目前仍缺乏能够通过简便用户交互分割多种解剖类别的通用基础分割模型。本文提出了一种名为SegVol的通用交互式三维医学图像分割模型。通过在9万例未标注的计算机断层扫描（CT）体数据和6千例标注CT数据上训练，该基础模型支持利用语义和空间提示分割超过200种解剖类别。大量实验证明，SegVol在多个分割基准上以显著优势超越当前最优方法。值得注意的是，在三个具有挑战性的病灶数据集上，本方法相较于nnU-Net取得了约20%的Dice系数提升。模型及数据已在https://github.com/BAAI-DCAI/SegVol 公开提供。