Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans
This work addresses the need for non-invasive plaque analysis to guide patient management in cardiology, representing an incremental improvement over existing methods.
The paper tackled the problem of predicting revascularization decisions from coronary artery plaque characterization in CCTA scans using deep learning, improving AUC from 0.80 to 0.90 for a 3D-RCNN and from 0.85 to 0.90 for a multi-view ensemble, with a new 2.5D approach achieving 0.90.
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this work we compare and improve three deep learning algorithms for this task: A 3D recurrent convolutional neural network (RCNN), a 2D multi-view ensemble approach based on texture analysis, and a newly proposed 2.5D approach. Current state of the art methods utilising fluid dynamics based fractional flow reserve (FFR) simulation reach an AUC of up to 0.93 for the task of predicting an abnormal invasive FFR value. For the comparable task of predicting revascularisation decision, we are able to improve the performance in terms of AUC of both existing approaches with the proposed modifications, specifically from 0.80 to 0.90 for the 3D-RCNN, and from 0.85 to 0.90 for the multi-view texture-based ensemble. The newly proposed 2.5D approach achieves comparable results with an AUC of 0.90.