Perivascular space Identification Nnunet for Generalised Usage (PINGU)

TL;DR

This paper introduces PINGU, a deep learning model based on nnUNet, trained on diverse MRI datasets to improve the generalization of perivascular space segmentation across different image qualities and sites.

Contribution

The study develops and evaluates PINGU, a robust PVS segmentation tool that outperforms existing methods, especially in basal ganglia, across heterogeneous datasets.

Findings

PINGU achieved voxel and cluster dice scores of 0.50 and 0.63 in white matter.

Performance drops on unseen site data but remains superior to other algorithms.

Training on diverse data improves generalization across different MRI qualities.

Abstract

Perivascular spaces(PVSs) form a central component of the brain\'s waste clearance system, the glymphatic system. These structures are visible on MRI images, and their morphology is associated with aging and neurological disease. Manual quantification of PVS is time consuming and subjective. Numerous deep learning methods for PVS segmentation have been developed, however the majority have been developed and evaluated on homogenous datasets and high resolution scans, perhaps limiting their applicability for the wide range of image qualities acquired in clinic and research. In this work we train a nnUNet, a top-performing biomedical image segmentation algorithm, on a heterogenous training sample of manually segmented MRI images of a range of different qualities and resolutions from 6 different datasets. These are compared to publicly available deep learning methods for 3D segmentation of PVS. The resulting model, PINGU (Perivascular space Identification Nnunet for Generalised Usage), achieved voxel and cluster level dice scores of 0.50(SD=0.15), 0.63(0.17) in the white matter(WM), and 0.54(0.11), 0.66(0.17) in the basal ganglia(BG). Performance on data from unseen sites was substantially lower for both PINGU(0.20-0.38(WM, voxel), 0.29-0.58(WM, cluster), 0.22-0.36(BG, voxel), 0.46-0.60(BG, cluster)) and the publicly available algorithms(0.18-0.30(WM, voxel), 0.29-0.38(WM cluster), 0.10-0.20(BG, voxel), 0.15-0.37(BG, cluster)), but PINGU strongly outperformed the publicly available algorithms, particularly in the BG. Finally, training PINGU on manual segmentations from a single site with homogenous scan properties gave marginally lower performances on internal cross-validation, but in some cases gave higher performance on external validation. PINGU stands out as broad-use PVS segmentation tool, with particular strength in the BG, an area of PVS related to vascular disease and pathology.