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

arXiv: https://arxiv.org/abs/2208.07167 · HTML: https://mlsanity.com/paper/2208.07167/

## 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 (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [Ranger21: a synergistic deep learning optimizer](https://mlsanity.com/paper/2106.13731/) (2021)
- [blob loss: instance imbalance aware loss functions for semantic segmentation](https://mlsanity.com/paper/2205.08209/) (2022)
- [Let's agree to disagree: learning highly debatable multirater labelling](https://mlsanity.com/paper/1909.01891/) (2019)

## Built on by (most cited first, 14 of 14)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps](https://mlsanity.com/paper/2312.02608/) (2023)
- [Approaching Peak Ground Truth](https://mlsanity.com/paper/2301.00243/) (2022)
- [A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge](https://mlsanity.com/paper/2403.19425/) (2024)
- [Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation](https://mlsanity.com/paper/2511.17146/) (2025)
- [A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces](https://mlsanity.com/paper/2603.04243/) (2026)
- [Perivascular space Identification Nnunet for Generalised Usage (PINGU)](https://mlsanity.com/paper/2405.08337/) (2024)
- [Automated neuroradiological support systems for multiple cerebrovascular disease markers -- A systematic review and meta-analysis](https://mlsanity.com/paper/2410.17124/) (2024)
- [Fully Guided Neural Schr"odinger bridge for Brain MR image synthesis](https://mlsanity.com/paper/2501.14171/) (2025)
- [Standardized Evaluation of Automatic Methods for Perivascular Spaces Segmentation in MRI -- MICCAI 2024 Challenge Results](https://mlsanity.com/paper/2512.18197/) (2025)
- [How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks](https://mlsanity.com/paper/2607.05329/) (2026)
- [BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation](https://mlsanity.com/paper/2609.30223/) (2026)
- [Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI](https://mlsanity.com/paper/2609.37387/) (2026)
- [Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings](https://mlsanity.com/paper/2610.01542/) (2026)

## 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.