# Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

arXiv: https://arxiv.org/abs/1904.11319 · HTML: https://mlsanity.com/paper/1904.11319/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 20 of 20)

- [Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization](https://mlsanity.com/paper/2103.13041/) (2021)
- [Learning the Effect of Registration Hyperparameters with HyperMorph](https://mlsanity.com/paper/2203.16680/) (2022)
- [Neuralizer: General Neuroimage Analysis without Re-Training](https://mlsanity.com/paper/2305.02644/) (2023)
- [Self-Supervised Longitudinal Neighbourhood Embedding](https://mlsanity.com/paper/2103.03840/) (2021)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [Deep Learning for Medical Image Registration: A Comprehensive Review](https://mlsanity.com/paper/2204.11341/) (2022)
- [Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction](https://mlsanity.com/paper/2103.02768/) (2021)
- [Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts](https://mlsanity.com/paper/2108.11926/) (2021)
- [I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2301.01149/) (2023)
- [Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation](https://mlsanity.com/paper/2103.16344/) (2021)
- [Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training](https://mlsanity.com/paper/2108.12280/) (2021)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease](https://mlsanity.com/paper/2104.14116/) (2021)
- [Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI](https://mlsanity.com/paper/2309.13777/) (2023)
- [Segmentation of 2D Brain MR Images](https://mlsanity.com/paper/2111.03370/) (2021)
- [Secure & Private Federated Neuroimaging](https://mlsanity.com/paper/2205.05249/) (2022)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)
- [Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior](https://mlsanity.com/paper/2403.18878/) (2024)
- [Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD](https://mlsanity.com/paper/2405.16248/) (2024)

## Abstract (arXiv)

Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU. The code is freely available at http://voxelmorph.mit.edu.