SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
Numi Sveinsson Cepero, Shawn C. Shadden
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引用次数: 0

Abstract

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.

Abstract Image

SeqSeg:为自动构建血管模型学习局部分段
心血管功能的计算建模已成为诊断、治疗和了解心血管疾病的关键部分。大多数策略都涉及构建解剖学上精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(序列分割):一种基于深度学习的新型自动追踪和分割算法,用于构建基于图像的血管模型。SeqSeg 利用基于 U-Net 的局部推理,对医学图像卷中的血管结构进行顺序分割。我们在主动脉和主动脉模型的 CT 和 MR 图像上测试了 SeqSeg,并将其预测结果与基准二维和三维全局 nnU-Net 模型的预测结果进行了比较。我们证明,SeqSeg 能够分割更完整的血管,并能泛化到训练数据中未注释的血管结构。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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