  We present a deep learning strategy that enables, for the first time,
contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI
scans, without requiring additional training or fine-tuning for new modalities.
Classical Bayesian methods address this segmentation problem with unsupervised
intensity models, but require significant computational resources. In contrast,
learning-based methods can be fast at test time, but are sensitive to the data
available at training. Our proposed learning method, SynthSeg, leverages a set
of training segmentations (no intensity images required) to generate synthetic
sample images of widely varying contrasts on the fly during training. These
samples are produced using the generative model of the classical Bayesian
segmentation framework, with randomly sampled parameters for appearance,
deformation, noise, and bias field. Because each mini-batch has a different
synthetic contrast, the final network is not biased towards any MRI contrast.
We comprehensively evaluate our approach on four datasets comprising over 1,000
subjects and four types of MR contrast. The results show that our approach
successfully segments every contrast in the data, performing slightly better
than classical Bayesian segmentation, and three orders of magnitude faster.
Moreover, even within the same type of MRI contrast, our strategy generalizes
significantly better across datasets, compared to training using real images.
Finally, we find that synthesizing a broad range of contrasts, even if
unrealistic, increases the generalization of the neural network. Our code and
model are open source at https://github.com/BBillot/SynthSeg.
