  The success of deep convolutional neural networks is partially attributed to
the massive amount of annotated training data. However, in practice, medical
data annotations are usually expensive and time-consuming to be obtained.
Considering multi-modality data with the same anatomic structures are widely
available in clinic routine, in this paper, we aim to exploit the prior
knowledge (e.g., shape priors) learned from one modality (aka., assistant
modality) to improve the segmentation performance on another modality (aka.,
target modality) to make up annotation scarcity. To alleviate the learning
difficulties caused by modality-specific appearance discrepancy, we first
present an Image Alignment Module (IAM) to narrow the appearance gap between
assistant and target modality data.We then propose a novel Mutual Knowledge
Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge
to facilitate the target-modality segmentation. To be specific, we formulate
our framework as an integration of two individual segmentors. Each segmentor
not only explicitly extracts one modality knowledge from corresponding
annotations, but also implicitly explores another modality knowledge from its
counterpart in mutual-guided manner. The ensemble of two segmentors would
further integrate the knowledge from both modalities and generate reliable
segmentation results on target modality. Experimental results on the public
multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method
achieves large improvements on CT segmentation by utilizing additional MRI data
and outperforms other state-of-the-art multi-modality learning methods.
