# An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation

arXiv: https://arxiv.org/abs/2207.04534 · HTML: https://mlsanity.com/paper/2207.04534/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 99 references that are in the map.
- First posted on arXiv 10 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2005.05135/) (2020)
- [Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2004.03675/) (2020)
- [A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2008.05117/) (2020)

## Built on by (most cited first, 9 of 9)

- [Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting](https://mlsanity.com/paper/2409.13416/) (2024)
- [Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data](https://mlsanity.com/paper/2211.02400/) (2022)
- [A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification](https://mlsanity.com/paper/2604.26807/) (2026)
- [USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR](https://mlsanity.com/paper/2311.08371/) (2023)
- [Deep generative priors for 3D brain analysis](https://mlsanity.com/paper/2510.15119/) (2025)
- [MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation](https://mlsanity.com/paper/2510.17529/) (2025)
- [TimeLesSeg: Unified Contrast-Agnostic Cross-Sectional and Longitudinal MS Lesion Segmentation via a Stochastic Generative Model](https://mlsanity.com/paper/2605.07955/) (2026)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)
- [Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study](https://mlsanity.com/paper/2609.09424/) (2026)

## Abstract (arXiv)

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.