A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces

• URL: http://arxiv.org/abs/2603.04243v2
• Date: Thu, 05 Mar 2026 15:19:39 GMT
• Title: A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces
• Authors: Lucas He, Krinos Li, Hanyuan Zhang, Runlong He, Silvia Ingala, Luigi Lorenzini, Marleen de Bruijne, Frederik Barkhof, Rhodri Davies, Carole Sudre,
• Abstract summary: Cerebral small vessel disease (CSVD) markers, specifically enlarged perivascular spaces (EPVS) and lacunae, present a unique challenge in medical image analysis.<n>We propose a morphologydecoupled framework where Zero- Gated CrossTask Attention exploits dense EPVS context to guide sparse lacune detection.
• Score: 3.9313804276175506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cerebral small vessel disease (CSVD) markers, specifically enlarged perivascular spaces (EPVS) and lacunae, present a unique challenge in medical image analysis due to their radiological mimicry. Standard segmentation networks struggle with feature interference and extreme class imbalance when handling these divergent targets simultaneously. To address these issues, we propose a morphology-decoupled framework where Zero-Initialized Gated Cross-Task Attention exploits dense EPVS context to guide sparse lacune detection. Furthermore, biological and topological consistency are enforced via a mixed-supervision strategy integrating Mutual Exclusion and Centerline Dice losses. Finally, we introduce an Anatomically-Informed Inference Calibration mechanism to dynamically suppress false positives based on tissue semantics. Extensive 5-folds cross-validation on the VALDO 2021 dataset (N=40) demonstrates state-of-the-art performance, notably surpassing task winners in lacunae detection precision (71.1%, p=0.01) and F1-score (62.6%, p=0.03). Furthermore, evaluation on the external EPAD cohort (N=1762) confirms the model's robustness for large-scale population studies. Code will be released upon acceptance.

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