Image dataset for a CNN algorithm development to detect coronary atherosclerosis in coronary CT angiography

Published: 8 November 2019| Version 1 | DOI: 10.17632/fk6rys63h9.1
Mutlu Demirer,
Vikash Gupta,
Matthew Bigelow,
Barbaros Erdal,
Luciano Prevedello,
Richard White


Coronary artery image sets for 500 patients. Each image represents a Mosaic Projection View (MPV) which consists of 18 different views of a straightened coronary artery stacked vertically. Training-Validation-Test image sets are sub-divided per patient with 3/1/1 ratio (300/100/100), each with 50% normal and 50% diseased cases. Artery images derived from the 300 training cases were augmented 6-fold to create 2,364 (i.e., 394 x 6) images in order to strengthen modeling and dataset balance. However, augmentation was not performed on the: 1. normal component of the training dataset (2,304 images); 2. entire validation dataset; or 3. entire testing dataset. In the validation dataset, only one artery was randomly selected per normal case (50 images) and diseased case (50 images) for balance maintenance.



Ohio State University Wexner Medical Center


Coronary Computed Tomography Angiography, Deep Neural Network