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.

一个基于注意力机制的新型切割框架MFA-Net（多尺度特征融合注意力网络）在医学CT图像中分割器官感兴趣区域，通过学习多个尺度的特征图并与现有方法进行比较，实验结果表明MFA-Net在不同尺度的图像上产生更加精确的分割。

MFA-Net：肝肿瘤分割的多尺度特征融合注意力网络