Deep learning based detection of collateral circulation in coronary
  angiographies
Cosmin-Andrei Hatfaludi Daniel Bunescu Costin Florian Ciusdel Alex Serban Karl Bose Marc Oppel Stephanie Schroder Christopher Seehase Harald F. Langer Jeanette Erdmann Henry Nording Lucian Mihai Itu
Coronary artery disease (CAD) is the dominant cause of death and
hospitalization across the globe. Atherosclerosis, an inflammatory condition
that gradually narrows arteries and has potentially fatal effects, is the most
frequent cause of CAD. Nonetheless, the circulation regularly adapts in the
presence of atherosclerosis, through the formation of collateral arteries,
resulting in significant long-term health benefits. Therefore, timely detection
of coronary collateral circulation (CCC) is crucial for CAD personalized
medicine. We propose a novel deep learning based method to detect CCC in
angiographic images. Our method relies on a convolutional backbone to extract
spatial features from each frame of an angiography sequence. The features are
then concatenated, and subsequently processed by another convolutional layer
that processes embeddings temporally. Due to scarcity of data, we also
experiment with pretraining the backbone on coronary artery segmentation, which
improves the results consistently. Moreover, we experiment with few-shot
learning to further improve performance, given our low data regime. We present
our results together with subgroup analyses based on Rentrop grading,
collateral flow, and collateral grading, which provide valuable insights into
model performance. Overall, the proposed method shows promising results in
detecting CCC, and can be further extended to perform landmark based CCC
detection and CCC quantification.