A Comprehensive Framework for Automated Segmentation of Perivascular Spaces in Brain MRI with the nnU-Net

• URL: http://arxiv.org/abs/2411.19564v2
• Date: Fri, 14 Feb 2025 06:44:21 GMT
• Title: A Comprehensive Framework for Automated Segmentation of Perivascular Spaces in Brain MRI with the nnU-Net
• Authors: William Pham, Alexander Jarema, Donggyu Rim, Zhibin Chen, Mohamed S. H. Khlif, Vaughan G. Macefield, Luke A. Henderson, Amy Brodtmann,
• Abstract summary: Enlargement of perivascular spaces (PVS) is common in neurodegenerative disorders.<n>There is a need for reliable PVS detection methods which are currently lacking.
• Score: 37.179674347248266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Enlargement of perivascular spaces (PVS) is common in neurodegenerative disorders including cerebral small vessel disease, Alzheimer's disease, and Parkinson's disease. PVS enlargement may indicate impaired clearance pathways and there is a need for reliable PVS detection methods which are currently lacking. Aim: To optimise a widely used deep learning model, the no-new-UNet (nnU-Net), for PVS segmentation. Methods: In 30 healthy participants (mean$\pm$SD age: 50$\pm$18.9 years; 13 females), T1-weighted MRI images were acquired using three different protocols on three MRI scanners (3T Siemens Tim Trio, 3T Philips Achieva, and 7T Siemens Magnetom). PVS were manually segmented across ten axial slices in each participant. Segmentations were completed using a sparse annotation strategy. In total, 11 models were compared using various strategies for image handling, preprocessing and semi-supervised learning with pseudo-labels. Model performance was evaluated using 5-fold cross validation (5FCV). The main performance metric was the Dice Similarity Coefficient (DSC). Results: The voxel-spacing agnostic model (mean$\pm$SD DSC=64.3$\pm$3.3%) outperformed models which resampled images to a common resolution (DSC=40.5-55%). Model performance improved substantially following iterative label cleaning (DSC=85.7$\pm$1.2%). Semi-supervised learning with pseudo-labels (n=12,740) from 18 additional datasets improved the agreement between raw and predicted PVS cluster counts (Lin's concordance correlation coefficient=0.89, 95%CI=0.82-0.94). We extended the model to enable PVS segmentation in the midbrain (DSC=64.3$\pm$6.5%) and hippocampus (DSC=67.8$\pm$5%). Conclusions: Our deep learning models provide a robust and holistic framework for the automated quantification of PVS in brain MRI.

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