# Perivascular space Identification Nnunet for Generalised Usage (PINGU)

arXiv: https://arxiv.org/abs/2405.08337 · HTML: https://mlsanity.com/paper/2405.08337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks](https://mlsanity.com/paper/1906.01891/) (2019)
- [Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021](https://mlsanity.com/paper/2208.07167/) (2022)
- [Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging](https://mlsanity.com/paper/1704.06033/) (2017)
- [GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network](https://mlsanity.com/paper/1705.07999/) (2017)
- [3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects](https://mlsanity.com/paper/1812.09046/) (2018)
- [3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI](https://mlsanity.com/paper/1802.05914/) (2018)
- [Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network](https://mlsanity.com/paper/1907.12452/) (2019)
- [Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI](https://mlsanity.com/paper/2209.13727/) (2022)

## Abstract (arXiv)

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.