mlsanityData to 14 Oct 2026

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

arXiv 2208.07167 ↗First posted 15 Aug 2022cs.CV cs.AIcited by 14

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Abstract (arXiv)

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the textitVAscular Lesions DetectiOn and Segmentation (textitWhere is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

14 of 57 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2017Mask R-CNNcited by 7,277
  5. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  7. 2021The Medical Segmentation Decathloncited by 628
  8. 2019Automated brain extraction of multi-sequence MRI using artificial neural networkscited by 134
  9. 2019Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challengecited by 115
  10. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60
  11. 2019BIAS: Transparent reporting of biomedical image analysis challengescited by 57
  12. 2021Ranger21: a synergistic deep learning optimizercited by 22
2 more
  1. 2022blob loss: instance imbalance aware loss functions for semantic segmentationcited by 18
  2. 2019Let's agree to disagree: learning highly debatable multirater labellingcited by 16

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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 4; 2026: 5.

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