Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning

  • yu xu xiang College of Computer Science and Information Technology, Central South University of Forestry & Technology
  • Yun Tan
Keywords: Aortic dissection detection, morphology, DenseNet


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 open 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.

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