# Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

arXiv: https://arxiv.org/abs/2004.10221 · HTML: https://mlsanity.com/paper/2004.10221/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI](https://mlsanity.com/paper/2112.09654/) (2021)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [MultiMorph: On-demand Atlas Construction](https://mlsanity.com/paper/2504.00247/) (2025)
- [Boosting Skull-Stripping Performance for Pediatric Brain Images](https://mlsanity.com/paper/2402.16634/) (2024)
- [Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior](https://mlsanity.com/paper/2504.11286/) (2025)
- [Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels](https://mlsanity.com/paper/2112.06693/) (2021)
- [Accurate super-resolution low-field brain MRI](https://mlsanity.com/paper/2202.03564/) (2022)
- [An MRF-UNet Product of Experts for Image Segmentation](https://mlsanity.com/paper/2104.05495/) (2021)
- [Wide Range MRI Artifact Removal with Transformers](https://mlsanity.com/paper/2210.07976/) (2022)
- [Towards contrast-agnostic soft segmentation of the spinal cord](https://mlsanity.com/paper/2310.15402/) (2023)
- [VINNA for Neonates -- Orientation Independence through Latent Augmentations](https://mlsanity.com/paper/2311.17546/) (2023)
- [Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI](https://mlsanity.com/paper/2305.03413/) (2023)
- [Simulation of acquisition shifts in T2 Flair MR images to stress test AI segmentation networks](https://mlsanity.com/paper/2311.01894/) (2023)

## Abstract (arXiv)

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image intensities that may not be representative of any one of the underlying classes. PV is particularly problematic for segmentation when there is a large resolution gap between the atlas and the test scan, e.g., when segmenting clinical scans with thick slices, or when using a high-resolution atlas. In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) low resolution (LR) scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates LR images from HR label maps with a generative model of PV, and can be trained to segment scans of any desired target contrast and resolution, even for previously unseen modalities where neither images nor segmentations are available at training. PV-SynthSeg does not require any preprocessing, and runs in seconds. We demonstrate the accuracy and flexibility of the method with extensive experiments on three datasets and 2,680 scans. The code is available at https://github.com/BBillot/SynthSeg.