  Automatic segmentation of the prostate cancer from the multi-modal magnetic
resonance images is of critical importance for the initial staging and
prognosis of patients. However, how to use the multi-modal image features more
efficiently is still a challenging problem in the field of medical image
segmentation. In this paper, we develop a cross-modal self-attention
distillation network by fully exploiting the encoded information of the
intermediate layers from different modalities, and the extracted attention maps
of different modalities enable the model to transfer the significant spatial
information with more details. Moreover, a novel spatial correlated feature
fusion module is further employed for learning more complementary correlation
and non-linear information of different modality images. We evaluate our model
in five-fold cross-validation on 358 MRI with biopsy confirmed. Extensive
experiment results demonstrate that our proposed network achieves
state-of-the-art performance.
