用深度学习自动分析单心室患者血流，识别关键血管与血流模式。
MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
- 两模型协同：先分割五类血管，再聚类血流时间序列。
- 血管分割平均Dice达0.91，全自动化处理超5500例影像。
- 发现不同血流表型与死亡/移植、肝病风险显著相关。
我们提出一个深度学习框架MultiFlow，用于单心室患者队列中基于速度编码相位对比磁共振（PCMR）数据的自动化分割与大规模血流表型分析。MultiFlowSeg同时对五条关键血管——左/右肺动脉、主动脉、上腔静脉和下腔静脉——进行分类与分割，训练数据来自260例心脏磁共振检查（每例含5次PCMR扫描），在50个未见测试病例上平均获得0.91的Dice评分。该方法被集成至全自动流程中，成功处理超过5500例注册病例，在所有5条血管均成功分割的前提下，分类准确率达98%，分割准确率为90%。基于成功分割的血流曲线，训练出MultiFlowDTC模型，采用深度时序聚类识别出不同的血流表型。生存分析显示，这些表型与更高的死亡或移植风险及肝病风险显著相关，凸显该框架的临床潜力。
原文摘要 · Abstract (English)
We present a deep learning framework with two models for automated segmentation and large-scale flow phenotyping in a registry of single-ventricle patients. MultiFlowSeg simultaneously classifies and segments five key vessels, left and right pulmonary arteries, aorta, superior vena cava, and inferior vena cava, from velocity encoded phase-contrast magnetic resonance (PCMR) data. Trained on 260 CMR exams (5 PCMR scans per exam), it achieved an average Dice score of 0.91 on 50 unseen test cases. The method was then integrated into an automated pipeline where it processed over 5,500 registry exams without human assistance, in exams with all 5 vessels it achieved 98% classification and 90% segmentation accuracy. Flow curves from successful segmentations were used to train MultiFlowDTC, which applied deep temporal clustering to identify distinct flow phenotypes. Survival analysis revealed distinct phenotypes were significantly associated with increased risk of death/transplantation and liver disease, demonstrating the potential of the framework.
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