  Assessing coronary artery plaque segments in coronary CT angiography scans is
an important task to improve patient management and clinical outcomes, as it
can help to decide whether invasive investigation and treatment are necessary.
In this work, we present three machine learning approaches capable of
performing this task. The first approach is based on radiomics, where a plaque
segmentation is used to calculate various shape-, intensity- and texture-based
features under different image transformations. A second approach is based on
deep learning and relies on centerline extraction as sole prerequisite. In the
third approach, we fuse the deep learning approach with radiomic features. On
our data the methods reached similar scores as simulated fractional flow
reserve (FFR) measurements, which - in contrast to our methods - requires an
exact segmentation of the whole coronary tree and often time-consuming manual
interaction. In literature, the performance of simulated FFR reaches an AUC
between 0.79-0.93 predicting an abnormal invasive FFR that demands
revascularization. The radiomics approach achieves an AUC of 0.86, the deep
learning approach 0.84 and the combined method 0.88 for predicting the
revascularization decision directly. While all three proposed methods can be
determined within seconds, the FFR simulation typically takes several minutes.
Provided representative training data in sufficient quantities, we believe that
the presented methods can be used to create systems for fully automatic
non-invasive risk assessment for a variety of adverse cardiac events.
