首个全自动高分辨率结肠分割工具，精度远超现有方法。
HQColon: A Hybrid Interactive Machine Learning Pipeline for High Quality Colon Labeling and Segmentation
- 结合区域生长与交互式机器学习构建高效标注流程
- 平均对称表面距离达0.2毫米，95%豪斯多夫距离仅1.0毫米
- 开源模型与数据集，适合医学影像研究与个性化医疗
高分辨率结肠分割对数字孪生和个性化医疗等临床与科研应用至关重要。然而，当前主流开源腹部分割工具TotalSegmentator在结肠分割上表现不佳，因其形状复杂多变，需耗时的人工标注。为此，我们提出首个全自动高分辨率结肠分割方法。首先，通过融合区域生长与交互式机器学习的管道，高效准确地标注了435例CT结肠成像（CTC）图像中的结肠。基于该数据集，训练了nnU-Net模型实现全自动分割。本方法平均对称表面距离为0.2毫米（对比TotalSegmentator的4.0毫米），95%豪斯多夫距离为1.0毫米（对比18毫米）。分割精度显著优于现有方法。我们公开了训练好的模型与完整代码，提供首个且唯一的开源高分辨率结肠分割工具。同时构建了大规模公开可用的高分辨率结肠标注数据集。
原文摘要 · Abstract (English)
High-resolution colon segmentation is crucial for clinical and research applications, such as digital twins and personalized medicine. However, the leading open-source abdominal segmentation tool, TotalSegmentator, struggles with accuracy for the colon, which has a complex and variable shape, requiring time-intensive labeling. Here, we present the first fully automatic high-resolution colon segmentation method. To develop it, we first created a high resolution colon dataset using a pipeline that combines region growing with interactive machine learning to efficiently and accurately label the colon on CT colonography (CTC) images. Based on the generated dataset consisting of 435 labeled CTC images we trained an nnU-Net model for fully automatic colon segmentation. Our fully automatic model achieved an average symmetric surface distance of 0.2 mm (vs. 4.0 mm from TotalSegmentator) and a 95th percentile Hausdorff distance of 1.0 mm (vs. 18 mm from TotalSegmentator). Our segmentation accuracy substantially surpasses TotalSegmentator. We share our trained model and pipeline code, providing the first and only open-source tool for high-resolution colon segmentation. Additionally, we created a large-scale dataset of publicly available high-resolution colon labels.
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