# MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds

arXiv: https://arxiv.org/abs/2108.02482 · HTML: https://mlsanity.com/paper/2108.02482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI](https://mlsanity.com/paper/2010.00148/) (2020)

## Built on by (most cited first, 1 of 1)

- [Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review](https://mlsanity.com/paper/2204.01411/) (2022)

## Abstract (arXiv)

Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects. In this work, we propose a multi-stage approach to both microbleed detection and segmentation. First, possible microbleed locations are detected with a Mask R-CNN technique. Second, at each possible microbleed location, a simple U-Net performs the final segmentation. This work used the 72 subjects as training data provided by the "Where is VALDO?" challenge of MICCAI 2021.