  Diagnosis and treatment guidance are aided by detecting relevant biomarkers
in medical images. Although supervised deep learning can perform accurate
segmentation of pathological areas, it is limited by requiring a-priori
definitions of these regions, large-scale annotations, and a representative
patient cohort in the training set. In contrast, anomaly detection is not
limited to specific definitions of pathologies and allows for training on
healthy samples without annotation. Anomalous regions can then serve as
candidates for biomarker discovery. Knowledge about normal anatomical structure
brings implicit information for detecting anomalies. We propose to take
advantage of this property using bayesian deep learning, based on the
assumption that epistemic uncertainties will correlate with anatomical
deviations from a normal training set. A Bayesian U-Net is trained on a
well-defined healthy environment using weak labels of healthy anatomy produced
by existing methods. At test time, we capture epistemic uncertainty estimates
of our model using Monte Carlo dropout. A novel post-processing technique is
then applied to exploit these estimates and transfer their layered appearance
to smooth blob-shaped segmentations of the anomalies. We experimentally
validated this approach in retinal optical coherence tomography (OCT) images,
using weak labels of retinal layers. Our method achieved a Dice index of 0.789
in an independent anomaly test set of age-related macular degeneration (AMD)
cases. The resulting segmentations allowed very high accuracy for separating
healthy and diseased cases with late wet AMD, dry geographic atrophy (GA),
diabetic macular edema (DME) and retinal vein occlusion (RVO). Finally, we
qualitatively observed that our approach can also detect other deviations in
normal scans such as cut edge artifacts.
