标题： SeqSeg：学习局部分割以自动构建血管模型 显示英文标题

标题： SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

摘要： 心血管功能的计算建模已成为诊断、治疗和理解心血管疾病的关键部分。 大多数策略涉及构建解剖学准确的心血管结构计算机模型，这是一个多步骤、耗时的过程。 为了改进模型生成过程，我们在此介绍SeqSeg（顺序分割）：一种基于深度学习的自动追踪和分割算法，用于构建基于图像的血管模型。 SeqSeg利用局部U-Net推理，从医学图像体积中依次分割血管结构。 我们在主动脉和主动脉股动脉模型的CT和MR图像上测试了SeqSeg，并将预测结果与基准2D和3D全局nnU-Net模型的预测结果进行了比较，这些模型之前在医学图像分割方面表现出色。 我们证明SeqSeg能够分割更完整的血管，并且能够推广到训练数据中未标注的血管结构。 显示英文摘要

摘要： Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.