In Medical physics ; h5-index 59.0
PURPOSE : The thrombus in the false lumen of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the false lumen in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy.
METHODS : A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features.
RESULTS : The proposed method provided accurate thrombus segmentation results (0.903±0.062 in dice score, 0.828±0.092 in Jaccard index, and 2.209±2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846±0.085 in dice score) and the method without skip connection attention refinement (0.899±0.060 in dice score). Moreover, the proposed method achieved 0.967±0.029 and 0.948±0.041 in dice score of true lumen and patent false lumen segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta.
CONCLUSIONS : A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application. This article is protected by copyright. All rights reserved.
Feng Hanying, Fu Zheng, Wang Yulin, Zhang Puming, Lai Hao, Zhao Jun
2022-Dec-21
Aortic dissection, CT-angiography, Convolution neural networks, Segmentation, Thrombus
