# 3D Convolutional Neural Networks for Stalled Brain Capillary Detection

arXiv: https://arxiv.org/abs/2104.01687 · HTML: https://mlsanity.com/paper/2104.01687/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [TernausNetV2: Fully Convolutional Network for Instance Segmentation](https://mlsanity.com/paper/1806.00844/) (2018)
- [Deep Learning for Automatic Pneumonia Detection](https://mlsanity.com/paper/2005.13899/) (2020)
- [Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models](https://mlsanity.com/paper/1801.00880/) (2018)
- [Breast Tumor Cellularity Assessment using Deep Neural Networks](https://mlsanity.com/paper/1905.01743/) (2019)
- [Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio](https://mlsanity.com/paper/1908.02924/) (2019)

## Built on by (most cited first, 8 of 8)

- [Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images](https://mlsanity.com/paper/2208.03934/) (2022)
- [How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis](https://mlsanity.com/paper/2309.00350/) (2023)
- [Unified Supervision For Vision-Language Modeling in 3D Computed Tomography](https://mlsanity.com/paper/2509.01554/) (2025)
- [Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks](https://mlsanity.com/paper/2301.03991/) (2023)
- [Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast](https://mlsanity.com/paper/2603.04113/) (2026)
- [A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis](https://mlsanity.com/paper/2211.03109/) (2022)
- [Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image](https://mlsanity.com/paper/2409.10952/) (2024)
- [A Geometric Multimodal Foundation Model Integrating Bp-MRI and Clinical Reports in Prostate Cancer Classification](https://mlsanity.com/paper/2602.00214/) (2026)

## Abstract (arXiv)

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.