Abstract 14467: Machine Learning for Identifying Salient Regions on the Coronary Artery on CT for Radiomic Feature Extraction and Characterization of CAD

Abstract

Introduction: Radiomics is a quantitative approach that allows for high throughput extraction of image features such as texture. It has been used for characterization of coronary artery disease (CAD). However, the reliability of radiomic features (RFs) varies with the image segmentation quality. We present a new strategy to establish a segmentation confidence map spatially across the coronary arteries in the dataset. We then evaluate the quality of RFs in salient (high confidence) and non-salient (low confidence) regions in the coronary artery. Purpose: To identify salient regions in the coronary artery, providing high segmentation certainty as well as a site for informative radiomic feature extraction, useful in CAD characterization. Methods: In our approach, we first applied a deep learning-based segmentation model (U-Net with ResNet backbone) to segment the coronary arteries (Fig. 1a). Next, we constructed a mean probability atlas from the segmentation probability maps, using fiducial registration (Fig. 1b). Finally, we identified salient and non-salient regions within the segmentation (Fig. 1c) by masking the mean probability atlas with the predicted segmentation and then thresholding them. Human expert-generated manual segmentation was used as ground truth. RFs—shape (26), first order (19) and second order (75)—were extracted from salient and non-salient regions for comparative correlation analysis with features from ground truth segmentation. A higher correlation implies that these features are equally informative as those from the manual segmentation. Results: The model achieved a dice score of 79.8% on the hold-out set. The RFs extracted only from the salient regions (Proximal portion of RCA and LAD) of the coronary artery display strong correlations with RFs extracted from manual segmentations (Fig 1d). Conclusion: We present a framework to identify regions for optimal extraction of RFs for improved CAD characterization.