Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning
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.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles published by TSP are under an Open Access license, which means all articles published by TSP are accessible online free of charge and as free of technical and legal barriers to everyone. Published materials can be re-used if properly acknowledged and cited Open Access publication is supported by the authors' institutes or research funding agencies by payment of a comparatively low Article Processing Charge (APC) for accepted articles.