  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.
