用深度学习自动分割血管，提升建模效率与泛化能力。
SeqSeg: Learning Local Segments for Automatic Vascular Model Construction
- 基于局部U-Net的序列化分割，逐步追踪血管结构。
- 在主动脉和下肢血管图像上实现更完整的血管分割。
- 可处理未标注的血管结构，适合临床建模应用。
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环节。现有方法多依赖于构建解剖精确的计算机模型，但过程复杂耗时。本文提出SeqSeg（序列分割）：一种基于深度学习的自动追踪与分割算法，用于构建基于影像的血管模型。该方法利用局部U-Net进行序列化推理，从医学图像体积中逐步分割血管结构。我们在主动脉及髂股动脉的CT和MR图像上测试了SeqSeg，将其预测结果与基准的2D和3D全局nnU-Net模型对比，后者此前在医学图像分割中表现出优异精度。实验表明，SeqSeg能更完整地分割血管结构，并可泛化至训练数据中未标注的血管形态。
原文摘要 · Abstract (English)
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.
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