期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 211, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2021.106417
关键词
Aorta dissection; Computed tomography; Deep learning; Image segmentation
类别
资金
- State's Key Project of Research and Development Plan [2017YFC0109202, 2018YFA0704102]
- National Natural Science Foundation [61871117]
- Science and Technology Program of Guangdong [2018B030333001]
This study proposed a deep-learning-based algorithm for segmenting dissected aorta, achieving high accuracy and robustness by combining 3-D and 2-D models. The edge extraction branch also improved segmentation accuracy near aorta boundaries.
Background and objective: Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed. Method: In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area. Results: The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%. Conclusions: The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps. (c) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据