  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.
