  Segmentation of organs of interest in medical CT images is beneficial for
diagnosis of diseases. Though recent methods based on Fully Convolutional
Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing
features from images with different scales is still a challenge: (1) Due to the
lack of spatial awareness, F-CNNs share the same weights at different spatial
locations. (2) F-CNNs can only obtain surrounding information through local
receptive fields. To address the above challenge, we propose a new segmentation
framework based on attention mechanisms, named MFA-Net (Multi-Scale Feature
Fusion Attention Network). The proposed framework can learn more meaningful
feature maps among multiple scales and result in more accurate automatic
segmentation. We compare our proposed MFA-Net with SOTA methods on two 2D liver
CT datasets. The experimental results show that our MFA-Net produces more
precise segmentation on images with different scales.
