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

arXiv: https://arxiv.org/abs/2603.04243 · HTML: https://mlsanity.com/paper/2603.04243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [ReZero is All You Need: Fast Convergence at Large Depth](https://mlsanity.com/paper/2003.04887/) (2020)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline](https://mlsanity.com/paper/1910.03866/) (2019)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging](https://mlsanity.com/paper/2406.05285/) (2024)
- [Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities](https://mlsanity.com/paper/1704.03379/) (2017)
- [Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021](https://mlsanity.com/paper/2208.07167/) (2022)
- [3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects](https://mlsanity.com/paper/1812.09046/) (2018)
- [3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI](https://mlsanity.com/paper/1802.05914/) (2018)
- [Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI](https://mlsanity.com/paper/2306.10622/) (2023)

## Built on by (most cited first, 1 of 1)

- [How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks](https://mlsanity.com/paper/2607.05329/) (2026)

## 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.