[Paper Review] Perivascular space Identification Nnunet for Generalised Usage (PINGU)

Benjamin Sinclair, Lucy Vivash+17 more|arXiv (Cornell University)|May 14, 2024

Neurosurgical Procedures and Complications5 citations

TL;DR

PINGU trains a nnUNet on heterogeneous MRI data to generalize perivascular space segmentation across varying image qualities, outperforming public methods, especially in the basal ganglia, though performance drops on unseen sites.

ABSTRACT

Perivascular spaces(PVSs) form a central component of the brainś 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.

Motivation & Objective

• Motivate robust PVS segmentation across heterogeneous MRI datasets.
• Develop a generalizable nnUNet-based model (PINGU) for PVS segmentation.
• Evaluate performance across white matter and basal ganglia against public baselines.
• Assess cross-site generalization and effects of training data homogeneity on external validation.

Proposed method

• Train nnUNet on manually segmented MRI data from six datasets with varying quality and resolution.
• Evaluate voxel- and cluster-level dice scores in WM and BG.
• Compare PINGU to publicly available 3D segmentation methods for PVS.
• Assess performance when training on a single-site homogeneous dataset vs multi-site heterogeneous data.

Experimental results

Research questions

• RQ1 Can a nnUNet-based PVS segmentation model generalize to diverse MRI qualities and sites?
• RQ2 How does PINGU perform in white matter vs basal ganglia relative to public methods?
• RQ3 What is the impact of training data homogeneity on external validation performance?
• RQ4 How does cross-site performance of PINGU compare to other algorithms on unseen data?

Key findings

• PINGU achieved voxel and cluster dice scores of 0.50 (SD=0.15) and 0.63 (0.17) in white matter, respectively.
• PINGU achieved voxel and cluster dice scores of 0.54 (0.11) and 0.66 (0.17) in basal ganglia, respectively.
• On unseen sites, PINGU performance dropped to 0.20-0.38 (WM voxel), 0.29-0.58 (WM cluster), 0.22-0.36 (BG voxel), 0.46-0.60 (BG cluster).
• Public algorithms showed lower ranges on unseen data (e.g., 0.18-0.30 WM voxel, 0.29-0.38 WM cluster, 0.10-0.20 BG voxel, 0.15-0.37 BG cluster).
• PINGU outperformed publicly available algorithms, particularly in the basal ganglia, an area linked to vascular disease and pathology.
• Training PINGU on manual segmentations from a single site with homogeneous scans yielded marginally lower internal cross-validation but sometimes higher external validation performance.