  It is necessary for clinicians to comprehensively analyze patient information
from different sources. Medical image fusion is a promising approach to
providing overall information from medical images of different modalities.
However, existing medical image fusion approaches ignore the semantics of
images, making the fused image difficult to understand. In this work, we
propose a new evaluation index to measure the semantic loss of fused image, and
put forward a Fusion W-Net (FW-Net) for multimodal medical image fusion. The
experimental results are promising: the fused image generated by our approach
greatly reduces the semantic information loss, and has better visual effects in
contrast to five state-of-art approaches. Our approach and tool have great
potential to be applied in the clinical setting.
