mlsanityData to 7 Oct 2026

MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds

arXiv 2108.02482 ↗First posted 5 Aug 2021eess.IV cs.CVcited by 1

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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

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Mask R-CNNcited by 7,259
  3. 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.