自动检测肠道CTE影像中的血管增生标志，提升克罗恩病诊断效率
AutoComb: Automated Comb Sign Detector for 3D CTE Scans
- 通过多阶段算法融合深度学习与统计模型，定位微小血管分支和肠壁强化
- 在真实临床数据上实现高准确率检测，避免人工阅片主观误差
- 适合放射科医生和消化内科医师用于辅助诊断炎症性肠病
Comb Sign 是一种重要的影像生物标志物，可提示多种胃肠道疾病。其表现为肠壁沿线状血流增加，提示可能存在异常，有助于医生诊断炎症性疾病。尽管具有重要临床意义，现有检测方法依赖人工操作，耗时且易受多平面图像方向影响而产生主观偏差。据我们所知，这是首个针对 CTE 扫描提出完全自动化 Comb Sign 检测的技术。本方法基于概率图构建，通过分步算法模块识别细小血管分叉与肠壁强化：包括使用深度学习分割模型、高斯混合模型（GMM）、基于 vesselness 滤波的血管提取、通过邻域最大化迭代增强血管显著性，以及基于距离的加权策略。实验结果表明，该流程能有效识别 Comb Sign，提供一种客观、准确、可靠的工具，有助于提升克罗恩病及相关高血流状态疾病的诊断准确性。
原文摘要 · Abstract (English)
Comb Sign is an important imaging biomarker to detect multiple gastrointestinal diseases. It shows up as increased blood flow along the intestinal wall indicating potential abnormality, which helps doctors diagnose inflammatory conditions. Despite its clinical significance, current detection methods are manual, time-intensive, and prone to subjective interpretation due to the need for multi-planar image-orientation. To the best of our knowledge, we are the first to propose a fully automated technique for the detection of Comb Sign from CTE scans. Our novel approach is based on developing a probabilistic map that shows areas of pathological hypervascularity by identifying fine vascular bifurcations and wall enhancement via processing through stepwise algorithmic modules. These modules include utilising deep learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction using vesselness filter, iterative probabilistic enhancement of vesselness via neighborhood maximization and a distance-based weighting scheme over the vessels. Experimental results demonstrate that our pipeline effectively identifies Comb Sign, offering an objective, accurate, and reliable tool to enhance diagnostic accuracy in Crohn's disease and related hypervascular conditions where Comb Sign is considered as one of the important biomarkers.
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