  Adequate blood supply is critical for normal brain function. Brain
vasculature dysfunctions such as stalled blood flow in cerebral capillaries are
associated with cognitive decline and pathogenesis in Alzheimer's disease.
Recent advances in imaging technology enabled generation of high-quality 3D
images that can be used to visualize stalled blood vessels. However,
localization of stalled vessels in 3D images is often required as the first
step for downstream analysis, which can be tedious, time-consuming and
error-prone, when done manually. Here, we describe a deep learning-based
approach for automatic detection of stalled capillaries in brain images based
on 3D convolutional neural networks. Our networks employed custom 3D data
augmentations and were used weight transfer from pre-trained 2D models for
initialization. We used an ensemble of several 3D models to produce the winning
submission to the Clog Loss: Advance Alzheimer's Research with Stall Catchers
machine learning competition that challenged the participants with classifying
blood vessels in 3D image stacks as stalled or flowing. In this setting, our
approach outperformed other methods and demonstrated state-of-the-art results,
achieving 0.85 Matthews correlation coefficient, 85% sensitivity, and 99.3%
specificity. The source code for our solution is made publicly available.
