Handling Label Uncertainty on the Example of Automatic Detection of
  Shepherd's Crook RCA in Coronary CT Angiography
Felix Denzinger Michael Wels Oliver Taubmann Florian Kordon Fabian Wagner Stephanie Mehltretter Mehmet A. Gülsün Max Schöbinger Florian André Sebastian Buss Johannes Görich Michael Sühling Andreas Maier
Coronary artery disease (CAD) is often treated minimally invasively with a
catheter being inserted into the diseased coronary vessel. If a patient
exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical
norm variant of the coronary vasculature - the complexity of this procedure is
increased. Automated reporting of this variant from coronary CT angiography
screening would ease prior risk assessment. We propose a 1D convolutional
neural network which leverages a sequence of residual dilated convolutions to
automatically determine this norm variant from a prior extracted vessel
centerline. As the SC RCA is not clearly defined with respect to concrete
measurements, labeling also includes qualitative aspects. Therefore, 4.23%
samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs,
with 5.97% being labeled as sure SC RCAs. We explore measures to handle this
label uncertainty, namely global/model-wise random assignment, exclusion, and
soft label assignment. Furthermore, we evaluate how this uncertainty can be
leveraged for the determination of a rejection class. With our best
configuration, we reach an area under the receiver operating characteristic
curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of
up to 0.020 AUC when rejecting 10% of the data and leveraging the labeling
uncertainty information in the exclusion process.