mlsanityData to 7 Oct 2026

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

arXiv 2603.04243 ↗First posted 4 Mar 2026cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 4 Mar 2026.

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16 of 25 references.

References · 16

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4 more
  1. 2022Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021cited by 14
  2. 20183D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objectscited by 5
  3. 20183D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRIcited by 4
  4. 2023Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRIcited by 1

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