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

• DOI: 10.1016/j.media.2023.103029
• Publication Date: 2023-11-19T22:31:26Z
• Authors: Carole H. Sudre; Kimberlin Van Wijnen; Florian Dubost; Hieab Adams; David Atkinson; Frederik Barkhof; Mahlet A. Birhanu; Esther E. Bron; Robin Camarasa; Nish Chaturvedi; Yuan Chen; Zihao Chen; Shuai Chen; Qi Dou; Tavia Evans; Ivan Ezhov; Haojun Gao; Marta Girones San...; Juan Domingo Gispert; Beatriz Gomez Anson; Alun D. Hughes; M. Arfan Ikram; Silvia Ingala; H. Rolf Jaeger; Florian Kofler; Hugo J. Kuijf; Denis Kutnar; Minho Lee; Bo Li; Luigi Lorenzini; Bjoern Menze; Jose Luis Molinuevo; Yiwei Pan; Elodie Puybareau; Rafael Rehwald; Ruisheng Su; Pengcheng Shi; Lorna Smith; Therese Tillin; Guillaume Tochon; Hélène Urien; Bas H.M. van der ...; Isabelle F. van d...; Benedikt Wiestler; Frank J. Wolters; Pinar Yilmaz; Marius de Groot; Meike W. Vernooij; Marleen de Bruijne
• Keywords: FOS: Computer and information sciences; Computer Science - Artificial Intelligence; Computers; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Reproducibility of Results; Automated; Brain; Challenge; CSVD; Detection; Enlarged perivascular spaces; Lacunes; Microbleeds; MRI; Segmentation; Magnetic Resonance Imaging; 03 medical and health sciences; 0302 clinical medicine; Artificial Intelligence (cs.AI); Cerebral Small Vessel Diseases; Humans; Cerebral Hemorrhage

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