Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 211, Issue -, Pages -Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2021.106417
Keywords
Aorta dissection; Computed tomography; Deep learning; Image segmentation
Categories
Funding
- State's Key Project of Research and Development Plan [2017YFC0109202, 2018YFA0704102]
- National Natural Science Foundation [61871117]
- Science and Technology Program of Guangdong [2018B030333001]
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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.
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