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Research Article


Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning

Yun Tan1, #, Ling Tan2, #, Xuyu Xiang1, *, Hao Tang2, *, Jiaohua Qin1, Wenyan Pan1
1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410114, China.
2 The Second Xiangya Hospital of Central South University, Changsha, 410011, China.
* Corresponding Authors: Xuyu Xiang. Email: ;
   Hao Tang. Email: .
   # The authors contributed equally to this work.


Aortic dissection (AD) is a kind of acute and rapidly progressing cardiovascular disease. In this work, we build a CTA image library with 88 CT cases, 43 cases of aortic dissection and 45 cases of health. An aortic dissection detection method based on CTA images is proposed. ROI is extracted based on binarization and morphology opening operation. The deep learning networks (InceptionV3, ResNet50, and DenseNet) are applied after the preprocessing of the datasets. Recall, F1-score, Matthews correlation coefficient (MCC) and other performance indexes are investigated. It is shown that the deep learning methods have much better performance than the traditional method. And among those deep learning methods, DenseNet121 can exceed other networks such as ResNet50 and InceptionV3.


Aortic dissection detection, morphology, DenseNet.
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