Presentation + Paper
3 March 2017 Coronary artery calcification (CAC) classification with deep convolutional neural networks
Xiuming Liu, Shice Wang, Yufeng Deng, Kuan Chen
Author Affiliations +
Abstract
Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC). Onequarter of the images were randomly selected as test samples; the rest were used as training samples. DCNN models consisting of 2,4,6 and 8 convolutional layers were designed using blocks of pre-designed CNN layers. Each block was implemented in Theano with Graphics Processing Units (GPU). Human-in-the-loop learning was also performed on a subset of 165 images with framed arteries by trained physicians. The results from the DCNN models were compared to the diagnostic reports. The average diagnostic accuracies for models with 2,4,6,8 layers were 0.85, 0.87, 0.88, and 0.89 respectively. The areas under the curve (AUC) were 0.92, 0.95, 0.95, and 0.96. As the model grows deeper, the AUC or diagnostic accuracies did not have statistically significant changes. The results of this study indicate that DCNN models have promising potential in the field of intelligent medical image diagnosis practice.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiuming Liu, Shice Wang, Yufeng Deng, and Kuan Chen "Coronary artery calcification (CAC) classification with deep convolutional neural networks", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340M (3 March 2017); https://doi.org/10.1117/12.2253974
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Arteries

Diagnostics

X-rays

X-ray imaging

Chest imaging

Convolutional neural networks

X-ray computed tomography

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