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
Contributors:
Mutlu Demirer,
Vikash Gupta,
Matthew Bigelow,
Barbaros Erdal,
Luciano Prevedello,
Richard White

Description

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.

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Institutions

Ohio State University Wexner Medical Center

Categories

Coronary Computed Tomography Angiography, Deep Neural Network

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