Perivascular space identification nnUNet for generalised usage (PINGU).

Area of Science:

• Neuroimaging
• Biomedical image analysis
• Deep learning for medical applications

Background:

• Perivascular spaces (PVS) are crucial for brain waste clearance via the glymphatic system.
• Enlarged PVS on MRI are linked to aging and neurological diseases.
• Manual PVS quantification is time-consuming and subjective, necessitating automated methods.

Purpose of the Study:

• To develop and evaluate a generalized deep learning model for automated PVS segmentation across diverse MRI qualities and resolutions.
• To compare the performance of the developed model against existing public deep learning algorithms for PVS segmentation.

Main Methods:

• A nnUNet deep learning model was trained on a heterogeneous dataset of manually segmented MRIs from 7 datasets and 6 scanners.
• The model, named PINGU (Perivascular space Identification Nnunet for Generalised Usage), was evaluated on various image qualities and resolutions.
• PINGU's performance was compared against two publicly available 3D PVS segmentation algorithms.

Main Results:

• PINGU achieved notable Dice scores in white matter (voxel: 0.50, cluster: 0.63) and basal ganglia (voxel: 0.54, cluster: 0.66).
• While performance decreased on external datasets, PINGU still outperformed existing algorithms, particularly in the basal ganglia.
• The model demonstrated superior generalization capabilities compared to current public PVS segmentation tools.

Conclusions:

• PINGU offers a robust and generalized solution for automated PVS segmentation, addressing limitations of existing methods.
• The model shows particular strength in segmenting PVS in the basal ganglia, a region relevant to vascular pathology.
• PINGU has the potential to be a valuable tool for research and clinical applications involving PVS analysis.