Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

Abstract

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 segmentation tools that are computationally efficient at test time. However, most of these strategies rely on supervised learning from manually annotated images and are therefore sensitive to the intensity profiles in the training dataset. A deep learning-based segmentation model for a new image dataset (e.g., of different contrast), usually requires a new labeled training dataset, which can be prohibitively expensive, or suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines conventional probabilistic atlas-based segmentation with deep learning, enabling training of 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 s at test time on a GPU.