MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 16 references that are in the map. First posted on arXiv 5 Aug 2021.
Builds on
3 of 16 references.
References · 3
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017Mask R-CNNcited by 7,259
- 2020DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRIcited by 6
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Citing papers per year: 2022: 1.
1 paper.