  Deep learning highly relies on the quantity of annotated data. However, the
annotations for 3D volumetric medical data require experienced physicians to
spend hours or even days for investigation. Self-supervised learning is a
potential solution to get rid of the strong requirement of training data by
deeply exploiting raw data information. In this paper, we propose a novel
self-supervised learning framework for volumetric medical images. Specifically,
we propose a context restoration task, i.e., Rubik's cube++, to pre-train 3D
neural networks. Different from the existing context-restoration-based
approaches, we adopt a volume-wise transformation for context permutation,
which encourages network to better exploit the inherent 3D anatomical
information of organs. Compared to the strategy of training from scratch,
fine-tuning from the Rubik's cube++ pre-trained weight can achieve better
performance in various tasks such as pancreas segmentation and brain tissue
segmentation. The experimental results show that our self-supervised learning
method can significantly improve the accuracy of 3D deep learning networks on
volumetric medical datasets without the use of extra data.
