Standardized evaluation of automatic methods for perivascular spaces segmentation in MRI – MICCAI 2024 challenge results

1. Med Image Anal. 2026 Jul 20;114:104227. doi: 10.1016/j.media.2026.104227.
Online ahead of print.

Standardized evaluation of automatic methods for perivascular spaces
segmentation in MRI – MICCAI 2024 challenge results.

Wu Y(1), Zhang Y(1), Dong Z(1), Ji F(1), Tan AS(2), Tan G(2), Tang S(2), Chen
H(1), Chen Z(1), Ng EKK(1), Bernal J(3), Min H(4), Xia Y(5), Vati I(6), Cooper
L(5), Hu X(7), Pei Y(7), Ma Y(7), Nozais V(8), Tsuchida A(9), Hervé PY(8),
Boutinaud P(8), Joliot M(10), Kang J(11), Kim W(11), Bak D(11), Hamadache
RE(12), Abramova V(12), Lladó X(12), Zhu Y(13), Gong Z(14), Chen X(15), McFadden
J(16), Khong PL(2), Duarte Coello R(16), Li HB(17), Koh WP(18), Chen C(19),
Wardlaw JM(16), Valdés Hernández MDC(20), Zhou JH(21).

Author information:
(1)Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic
Resonance Research (TMR), Yong Loo Lin School of Medicine, National University
of Singapore, Singapore.
(2)National University Hospital, Singapore.
(3)Institute for Neuroscience and Cardiovascular Research, Row Fogo Centre for
Research into Ageing and the Brain, Department of Neuroimaging Sciences, The
University of Edinburgh, Edinburgh, UK; German Centre for Neurodegenerative
Diseases (DZNE), Germany; Department Artificial Intelligence in Biomedical
Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),
Erlangen, Germany; Faculty of Medicine, Friedrich-Alexander-Universität
Erlangen-Nürnberg (FAU), Erlangen, Germany; Institute of Cognitive Neurology and
Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg,
Germany.
(4)Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston,
4029, Queensland, Australia; South Western Clinical School, University of New
South Wales, Sydney, Australia.
(5)Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston,
4029, Queensland, Australia.
(6)Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston,
4029, Queensland, Australia; School of Electrical Engineering and Robotics,
Queensland University of Technology, Brisbane, Australia.
(7)Central China Normal University, Wuhan, China.
(8)Fealinx, France.
(9)Groupe d’Imagerie Neurofonctionnelle (GIN), Institute of Neurodegenerative
Diseases (IMN), UMR5293, CNRS, CEA, University of Bordeaux, Bordeaux, France;
Bordeaux Population Health, INSERM, U1219, University of Bordeaux, Bordeaux,
France.
(10)Groupe d’Imagerie Neurofonctionnelle (GIN), Institute of Neurodegenerative
Diseases (IMN), UMR5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
(11)Department of Biomedical Engineering, Hankuk University of Foreign Studies,
Yongin, South Korea.
(12)Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de
Girona, Catalonia, Spain.
(13)School of Mathematics, Nanjing University, Nanjing, China.
(14)Department of Neurosurgery, Klinikum Rechts der Isar, Technical University
of Munich, Germany.
(15)Longhua Hospital Shanghai University of Traditional Chinese Medicine,
Shanghai, China.
(16)Centre for Clinical Brain Sciences, UK Dementia Research Institute at The
University of Edinburgh, The University of Edinburgh, Edinburgh, UK.
(17)Harvard Medical School, Boston, Massachusetts, USA.
(18)Healthy Longevity Translational Research Program, Yong Loo Lin School of
Medicine, National University of Singapore, Singapore.
(19)Department of Pharmacology, National University of Singapore, Singapore.
(20)Centre for Clinical Brain Sciences, UK Dementia Research Institute at The
University of Edinburgh, The University of Edinburgh, Edinburgh, UK. Electronic
address: .
(21)Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic
Resonance Research (TMR), Yong Loo Lin School of Medicine, National University
of Singapore, Singapore; Healthy Longevity Translational Research Program, Yong
Loo Lin School of Medicine, National University of Singapore, Singapore; Human
Potential Translational Research Program and Department of Medicine, Yong Loo
Lin School of Medicine, National University of Singapore, Singapore; Department
of Electrical and Computer Engineering, National University of Singapore,
Singapore. Electronic address: .

Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic
resonance imaging (MRI) structural sequences, are important imaging markers of
cerebral small vessel disease and potential indicators of neurodegenerative
conditions. Despite their clinical significance, automatic enlarged PVS (EPVS)
segmentation remains challenging due to their small size, variable morphology,
similarity with other pathological features, and limited annotated datasets.
This paper presents the EPVS Challenge organized at MICCAI 2024, which aims to
advance the development of automated algorithms for EPVS segmentation across
multi-site data. We provided a diverse dataset comprising 100 training, 50
validation, and 50 testing scans collected from multiple international sites
(UK, Singapore, and China) with varying MRI protocols and demographics. All
annotations followed the STRIVE protocol to ensure standardized ground truth and
covered the full brain parenchyma. Seven teams completed the full challenge,
implementing various deep learning approaches primarily based on U-Net
architectures with innovations in multi-modal processing, ensemble strategies,
and transformer-based components. Performance was evaluated using dice
similarity coefficient, absolute volume difference, recall, and precision
metrics. The winning method employed MedNeXt architecture with a dual 2D/3D
strategy for handling varying slice thicknesses. The top solutions showed
relatively good performance on test data from seen datasets, but significant
degradation of performance was observed on the previously unseen Shanghai
cohort, highlighting cross-site generalization challenges due to domain shift.
This challenge establishes an important benchmark for EPVS segmentation methods
and underscores the need for the continued development of robust algorithms that
can generalize in diverse clinical settings.

Copyright © 2026. Published by Elsevier B.V.

DOI: 10.1016/j.media.2026.104227
PMID: 42537273

Conflict of interest statement: Declaration of competing interest The authors
declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this
paper.

Auteurs Bordeaux Neurocampus