SegVol：通用且交互式的体医学图像分割
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
AI总结：
针对缺乏可交互分割多解剖类别的3D医学图像基础模型问题，提出SegVol模型，通过扩展训练数据、设计缩放机制，在22项任务中19项优于竞品，提升达37.24%，推动体医学图像分析发展。
摘要：
精确的图像分割为临床研究提供指导性信息。尽管医学图像分割已取得显著进展，但仍缺乏一种能通过简单用户交互分割多种解剖类别的3D基础分割模型。本文提出名为SegVol的3D基础分割模型，支持通用且交互式的体医学图像分割。通过将训练数据扩展至90K无标签计算机断层扫描（CT）体数据和6K有标签CT体数据，该基础模型可利用语义和空间提示分割200余种解剖类别。为实现体图像的高效精确推理，我们设计了缩放-放大机制。在22项解剖分割任务上的大量实验表明，SegVol在19项任务中优于竞争对手，相比亚军方法的提升高达37.24%。我们通过消融研究验证了特定设计的有效性和重要性。期望该基础模型能推动体医学图像分析的发展。模型和代码公开于：https://github.com/BAAI-DCAI/SegVol。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
