3D Convolutional Neural Networks for Stalled Brain Capillary Detection

TL;DR

This paper presents a deep learning approach using 3D convolutional neural networks for automatic detection of stalled brain capillaries in high-resolution 3D images, improving accuracy and efficiency in identifying vascular dysfunctions related to Alzheimer's disease.

Contribution

The study introduces a novel 3D CNN-based method with custom data augmentation and transfer learning, achieving state-of-the-art results in stalled capillary detection in brain images.

Findings

Achieved 0.85 Matthews correlation coefficient

Reached 85% sensitivity in detection

Achieved 99.3% specificity

Abstract

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.