# P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI

arXiv: https://arxiv.org/abs/2403.13996 · HTML: https://mlsanity.com/paper/2403.13996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Topology-Preserving Deep Image Segmentation](https://mlsanity.com/paper/1906.05404/) (2019)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology](https://mlsanity.com/paper/1910.01877/) (2019)
- [Topology-Aware Segmentation Using Discrete Morse Theory](https://mlsanity.com/paper/2103.09992/) (2021)
- [A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2005.05135/) (2020)
- [Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images](https://mlsanity.com/paper/1802.05203/) (2018)
- [Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities](https://mlsanity.com/paper/1610.04834/) (2016)
- [Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications](https://mlsanity.com/paper/2104.10029/) (2021)
- [Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs](https://mlsanity.com/paper/2008.02001/) (2020)

## Built on by (most cited first, 1 of 1)

- [Spatial Diffusion for Cell Layout Generation](https://mlsanity.com/paper/2409.03106/) (2024)

## Abstract (arXiv)

White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis. Automated WMH segmentation methods enable quantitative analysis via estimation of total lesion load, spatial distribution of lesions, and number of lesions (i.e., number of connected components after thresholding), all of which are correlated with patient outcomes. While the two former measures can generally be estimated robustly, the number of lesions is highly sensitive to noise and segmentation mistakes -- even when small connected components are eroded or disregarded. In this article, we present P-Count, an algebraic WMH counting tool based on persistent homology that accounts for the topological features of WM lesions in a robust manner. Using computational geometry, P-Count takes the persistence of connected components into consideration, effectively filtering out the noisy WMH positives, resulting in a more accurate count of true lesions. We validated P-Count on the ISBI2015 longitudinal lesion segmentation dataset, where it produces significantly more accurate results than direct thresholding.