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Imaging tasks like segmentation and registration are fundamental in a broad range of medical research studies. These tasks are increasingly solved by machine learning based methods. However, given the heterogeneity of medical imaging modalities, many existing methods are not able to generalize well to new modalities or even slight variations of existing modalities, and only perform well on the type of data they were trained on. Most practitioners have limited training data for a given task, limiting their ability to train generalized networks. To enable neural networks trained on one image type or modality to perform well on other imaging contrasts, we propose $\texttt{CIFL}$: contrast invariant feature learning. CIFL uses synthesized images of varying contrasts and artifacts, and an unsupervised loss function, to learn rich contrast-invariant image features. The resulting representation can be used as input to downstream tasks like segmentation or registration given some modality available at training, and subsequently enables performing that task on contrasts not available during training. In this paper, we perform experiments that demonstrate generalizability in brain segmentation and registration.
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