MultiFlow：在多中心单心室患者队列上验证的用于相位对比MRI多血管分类、分割和聚类的统一深度学习框架
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
AI总结：
提出包含MultiFlowSeg和MultiFlowDTC的深度学习框架，实现单心室患者PCMR数据的多血管自动分类分割与血流表型聚类，处理超5500例检查并发现表型与不良预后显著相关。
摘要：
我们提出了一个包含两个模型的深度学习框架，用于在单心室患者登记库中进行自动分割和大规模血流表型分析。MultiFlowSeg从速度编码相位对比磁共振（PCMR）数据中同时分类并分割五条关键血管：左肺动脉、右肺动脉、主动脉、上腔静脉和下腔静脉。该模型在260次CMR检查（每次检查5次PCMR扫描）上训练，在50个未见测试病例上达到了0.91的平均Dice分数。该方法随后被整合到自动化流程中，在无人工协助下处理了超过5,500次登记库检查；在包含全部5条血管的检查中，它达到了98%的分类准确率和90%的分割准确率。来自成功分割的血流曲线被用于训练MultiFlowDTC，该模型应用深度时间聚类来识别不同的血流表型。生存分析显示，不同表型与死亡/移植风险增加及肝脏疾病显著相关，展示了该框架的潜力。
英文摘要：
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.
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。
