# Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

arXiv: https://arxiv.org/abs/2209.02032 · HTML: https://mlsanity.com/paper/2209.02032/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/1901.08211/) (2019)
- [FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline](https://mlsanity.com/paper/1910.03866/) (2019)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders](https://mlsanity.com/paper/2006.13791/) (2020)
- [Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth](https://mlsanity.com/paper/1702.03407/) (2017)
- [A Learning Strategy for Contrast-agnostic MRI Segmentation](https://mlsanity.com/paper/2003.01995/) (2020)
- [An application of cascaded 3D fully convolutional networks for medical image segmentation](https://mlsanity.com/paper/1803.05431/) (2018)
- [Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks](https://mlsanity.com/paper/2012.10533/) (2020)
- [Deep Generative Model-based Quality Control for Cardiac MRI Segmentation](https://mlsanity.com/paper/2006.13379/) (2020)
- [Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast](https://mlsanity.com/paper/2004.10221/) (2020)
- [Incorporating prior knowledge in medical image segmentation: a survey](https://mlsanity.com/paper/1607.01092/) (2016)
- [A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology](https://mlsanity.com/paper/1806.08634/) (2018)
- [An Alarm System For Segmentation Algorithm Based On Shape Model](https://mlsanity.com/paper/1903.10645/) (2019)
- [Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining](https://mlsanity.com/paper/2203.01969/) (2022)

## Built on by (most cited first, 30 of 51)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis](https://mlsanity.com/paper/2411.02372/) (2024)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging](https://mlsanity.com/paper/2311.16914/) (2023)
- [SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI](https://mlsanity.com/paper/2312.13534/) (2023)
- [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)
- [Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation](https://mlsanity.com/paper/2309.05306/) (2023)
- [Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data](https://mlsanity.com/paper/2403.15103/) (2024)
- [MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI](https://mlsanity.com/paper/2405.05944/) (2024)
- [Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI](https://mlsanity.com/paper/2409.03889/) (2024)
- [Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation](https://mlsanity.com/paper/2409.16818/) (2024)
- [A 3D generative model of pathological multi-modal MR images and segmentations](https://mlsanity.com/paper/2311.04552/) (2023)
- [Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentation](https://mlsanity.com/paper/2411.16719/) (2024)
- [Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization](https://mlsanity.com/paper/2501.13370/) (2025)
- [Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation](https://mlsanity.com/paper/2411.06842/) (2024)
- [TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View](https://mlsanity.com/paper/2502.20637/) (2025)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI](https://mlsanity.com/paper/2308.12736/) (2023)
- [Evaluation of 3D Counterfactual Brain MRI Generation](https://mlsanity.com/paper/2508.02880/) (2025)
- [Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis](https://mlsanity.com/paper/2509.19711/) (2025)
- [Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation](https://mlsanity.com/paper/2308.02363/) (2023)
- [Opportunities and challenges in the application of large artificial intelligence models in radiology](https://mlsanity.com/paper/2403.16112/) (2024)
- [Guided Synthesis of Labeled Brain MRI Data Using Latent Diffusion Models for Segmentation of Enlarged Ventricles](https://mlsanity.com/paper/2411.01351/) (2024)
- [Integrating Anatomical Priors into a Causal Diffusion Model](https://mlsanity.com/paper/2509.09054/) (2025)
- [Cortex-Grounded Diffusion Models for Brain Image Generation](https://mlsanity.com/paper/2601.19498/) (2026)
- [Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma](https://mlsanity.com/paper/2603.08385/) (2026)
- [USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR](https://mlsanity.com/paper/2311.08371/) (2023)
- [SYNCS: Synthetic Data and Contrastive Self-Supervised Training for Central Sulcus Segmentation](https://mlsanity.com/paper/2403.15121/) (2024)
- [Deep learning-based brain segmentation model performance validation with clinical radiotherapy CT](https://mlsanity.com/paper/2406.17423/) (2024)

## Abstract (arXiv)

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artefacts, subject populations). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.