  Multi-modal magnetic resonance imaging (MRI) is essential in clinics for
comprehensive diagnosis and surgical planning. Nevertheless, the segmentation
of multi-modal MR images tends to be time-consuming and challenging.
Convolutional neural network (CNN)-based multi-modal MR image analysis commonly
proceeds with multiple down-sampling streams fused at one or several layers.
Although inspiring performance has been achieved, the feature fusion is usually
conducted through simple summation or concatenation without optimization. In
this work, we propose a supervised image fusion method to selectively fuse the
useful information from different modalities and suppress the respective noise
signals. Specifically, an attention block is introduced as guidance for the
information selection. From the different modalities, one modality that
contributes most to the results is selected as the master modality, which
supervises the information selection of the other assistant modalities. The
effectiveness of the proposed method is confirmed through breast mass
segmentation in MR images of two modalities and better segmentation results are
achieved compared to the state-of-the-art methods.
