Diffusion MRI Specific Pretraining by Self-supervision on an Auxiliary Dataset

Training deep learning networks is very data intensive. Especially in fields with a very limited number of annotated datasets, such as diffusion MRI, it is of great importance to develop approaches that can cope with a limited amount of data. It was previously shown that transfer learning can lead to better results and more stable training in various medical applications. However, the use of off-the-shelf transfer learning tools in high angular resolution diffusion MRI is not straightforward, as such 3D approaches are commonly designed for scalar data. Here, an extension of self-supervised pretraining to diffusion MRI data is presented, and enhanced with a modality-specific procedure, where artifacts encountered in diffusion MRI need to be removed. We pretrained on publicly available data from the Human Connectome Project and evaluated the success on data from a local hospital with three modality-related experiments: segmentation of brain microstructure, detection of fiber crossings, and regression of nerve fiber spatial orientation. The results were compared against a setting without pretraining, and against classical autoencoder pretraining. We find that it is possible to achieve both improved metrics and a more stable training with the proposed diffusion MRI specific pretraining procedure.