HEPATIC LESION SEGMENTATION BY COMBINING PLAIN AND CONTRAST-ENHANCED CT IMAGES WITH MODALITY WEIGHTED U-NET

We propose the Modality Weighted U-Net (MW-UNet) to combine plain Computed Tomography (CT) and Contrast-Enhanced Computed Tomography (CECT) images for hepatic lesion segmentation. Observing that CT and CECT images provide complimentary but different amount of information for the segmentation task, we propose to fuse their features at specific layers of the U-Net by the weighted sum rule. The weight parameters are updated through backpropagation during training. Compared with most combination methods which concatenate feature maps at the last or intermediate layers, the proposed method obtains feature level fusion with very simple combination rules. Thus, great amount of parameters and computation can be saved. We evaluate our model on the MCGHD database and demonstrate the superiority of the proposed method over other state-of-the-arts both in accuracy and computation.