Epistemic Uncertainty Modeling for Vessel Segmentation.

X-ray angiograms are currently the gold-standard in percutaneous guidance during cardiovascular interventions. However, due to lack of contrast, to overlapping artifacts and to the rapid dilution of the contrast agent, they remain difficult to analyze either by cardiologists, or automatically by computers. Providing, a general yet accurate multi-arteries segmentation method along with the uncertainty linked to those segmentations would not only ease the analysis of medical imaging by cardiologists, but also provide a required pre-processing of the data for tasks ranging from 3D reconstruction to motion tracking of arteries. The proposed method has been validated on clinical data providing an average accuracy of 94.9%. Additionally, results show good transposition of learning from one type of artery to another. Epistemic uncertainty maps provide areas where the segmentation should be validated by an expert before being used, and could provide identification of regions of interest for data augmentation purposes.