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

Research output: Contribution to journal › Journal article › Research › peer-review

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Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. / ALFA study.

In: Medical Image Analysis, Vol. 91, 103029, 2024.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

ALFA study 2024, 'Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021', Medical Image Analysis, vol. 91, 103029. https://doi.org/10.1016/j.media.2023.103029

APA

ALFA study (2024). Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Medical Image Analysis, 91, [103029]. https://doi.org/10.1016/j.media.2023.103029

Vancouver

ALFA study. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Medical Image Analysis. 2024;91. 103029. https://doi.org/10.1016/j.media.2023.103029

Author

ALFA study. / Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. In: Medical Image Analysis. 2024 ; Vol. 91.

Bibtex

@article{3de893be022d4f3e8df792c048966739,
title = "Where is VALDO?: VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021",
abstract = "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 VAscular Lesions DetectiOn and Segmentation (Where 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.",
author = "Sudre, {Carole H} and {Van Wijnen}, Kimberlin and Florian Dubost and Hieab Adams and David Atkinson and Frederik Barkhof and Birhanu, {Mahlet A} and Bron, {Esther E} and Robin Camarasa and Nish Chaturvedi and Yuan Chen and Zihao Chen and Shuai Chen and Qi Dou and Tavia Evans and Ivan Ezhov and Haojun Gao and {Girones Sanguesa}, Marta and Gispert, {Juan Domingo} and {Gomez Anson}, Beatriz and Hughes, {Alun D} and Ikram, {M Arfan} and Silvia Ingala and Jaeger, {H Rolf} and Florian Kofler and Kuijf, {Hugo J} and Denis Kutnar and Minho Lee and Bo Li and Luigi Lorenzini and Bjoern Menze and Molinuevo, {Jose Luis} and Yiwei Pan and Elodie Puybareau and Rafael Rehwald and Ruisheng Su and Pengcheng Shi and Lorna Smith and Therese Tillin and Guillaume Tochon and H{\'e}l{\`e}ne Urien and {van der Velden}, {Bas H M} and {van der Velpen}, {Isabelle F} and Benedikt Wiestler and Wolters, {Frank J} and Pinar Yilmaz and {de Groot}, Marius and Vernooij, {Meike W} and {de Bruijne}, Marleen and {ALFA study}",
note = "Copyright {\textcopyright} 2023 The Author(s). Published by Elsevier B.V. All rights reserved.",
year = "2024",
doi = "10.1016/j.media.2023.103029",
language = "English",
volume = "91",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Where is VALDO?

T2 - VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

AU - Sudre, Carole H

AU - Van Wijnen, Kimberlin

AU - Dubost, Florian

AU - Adams, Hieab

AU - Atkinson, David

AU - Barkhof, Frederik

AU - Birhanu, Mahlet A

AU - Bron, Esther E

AU - Camarasa, Robin

AU - Chaturvedi, Nish

AU - Chen, Yuan

AU - Chen, Zihao

AU - Chen, Shuai

AU - Dou, Qi

AU - Evans, Tavia

AU - Ezhov, Ivan

AU - Gao, Haojun

AU - Girones Sanguesa, Marta

AU - Gispert, Juan Domingo

AU - Gomez Anson, Beatriz

AU - Hughes, Alun D

AU - Ikram, M Arfan

AU - Ingala, Silvia

AU - Jaeger, H Rolf

AU - Kofler, Florian

AU - Kuijf, Hugo J

AU - Kutnar, Denis

AU - Lee, Minho

AU - Li, Bo

AU - Lorenzini, Luigi

AU - Menze, Bjoern

AU - Molinuevo, Jose Luis

AU - Pan, Yiwei

AU - Puybareau, Elodie

AU - Rehwald, Rafael

AU - Su, Ruisheng

AU - Shi, Pengcheng

AU - Smith, Lorna

AU - Tillin, Therese

AU - Tochon, Guillaume

AU - Urien, Hélène

AU - van der Velden, Bas H M

AU - van der Velpen, Isabelle F

AU - Wiestler, Benedikt

AU - Wolters, Frank J

AU - Yilmaz, Pinar

AU - de Groot, Marius

AU - Vernooij, Meike W

AU - de Bruijne, Marleen

AU - ALFA study

N1 - Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

PY - 2024

Y1 - 2024

N2 - 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 VAscular Lesions DetectiOn and Segmentation (Where 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.

AB - 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 VAscular Lesions DetectiOn and Segmentation (Where 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.

U2 - 10.1016/j.media.2023.103029

DO - 10.1016/j.media.2023.103029

M3 - Journal article

C2 - 37988921

VL - 91

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

M1 - 103029

ER -

ID: 375725811