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A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection

Korean Journal of Radiology 2021년 22권 2호 p.168 ~ 178
Yu Yitong, Gao Yang, Wei Jianyong, Liao Fangzhou, Xiao Qianjiang, Zhang Jie, Yin Weihua, Lu Bin,
소속 상세정보
 ( Yu Yitong ) - Chinese Academy of Medical Sciences and Peking Union Medical College Fuwai Hospital Department of Radiology
 ( Gao Yang ) - Chinese Academy of Medical Sciences and Peking Union Medical College Fuwai Hospital Department of Radiology
 ( Wei Jianyong ) - Shukun Beijing Technology Co. Ltd.
 ( Liao Fangzhou ) - Chinese Academy of Sciences Institute of Information Engineering
 ( Xiao Qianjiang ) - Shukun Beijing Technology Co. Ltd.
 ( Zhang Jie ) - Chinese Academy of Medical Sciences and Peking Union Medical College Fuwai Hospital Department of Radiology
 ( Yin Weihua ) - Chinese Academy of Medical Sciences and Peking Union Medical College Fuwai Hospital Department of Radiology
 ( Lu Bin ) - Chinese Academy of Medical Sciences and Peking Union Medical College Fuwai Hospital Department of Radiology

Abstract


Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD).

Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated.

Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were ?0.042 mm (?3.412 to 3.330 mm), ?0.376 mm (?3.328 to 2.577 mm), and 0.026 mm (?3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were ?0.166 mm (?1.419 to 1.086 mm), ?0.050 mm (?0.970 to 1.070 mm), and ?0.085 mm (?1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001).

Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

키워드

Aortic dissection; Tomography, X-ray computed; Deep learning

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