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

Lucas He, Krinos Li, Hanyuan Zhang, Runlong He, Silvia Ingala, Luigi Lorenzini, Marleen de Bruijne, Frederik Barkhof, Rhodri Davies, Carole Sudre

TL;DR

A morphology-decoupled framework where Zero-Initialized Gated Cross-Task Attention exploits dense EPVS context to guide sparse lacune detection and an Anatomically-Informed Inference Calibration mechanism to dynamically suppress false positives based on tissue semantics is proposed.

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.

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

TL;DR

A morphology-decoupled framework where Zero-Initialized Gated Cross-Task Attention exploits dense EPVS context to guide sparse lacune detection and an Anatomically-Informed Inference Calibration mechanism to dynamically suppress false positives based on tissue semantics is proposed.

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.
Paper Structure (12 sections, 4 equations, 2 figures, 3 tables)

This paper contains 12 sections, 4 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Multi-Task Network Architecture
  4. Anatomically-Informed Inference Calibration
  5. Objective Function
  6. Experiments and Results
  7. Datasets
  8. Implementation Details
  9. Evaluation Metrics
  10. Main Results
  11. Ablation Studies
  12. Conclusion

Figures (2)

  • Figure 1: Overview of the Proposed Morphology-Decoupled Framework.
  • Figure 2: Qualitative visualization on the VALDO dataset. Ground truth lesions (cyan), true positive predictions (green), and false positive predictions (red) are shown.