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.

翻译：医学CT图像中感兴趣器官的分割有助于疾病诊断。尽管基于全卷积神经网络（F-CNNs）的最新方法已在许多分割任务中取得成功，但融合不同尺度图像特征仍面临挑战：（1）由于缺乏空间感知能力，F-CNNs在不同空间位置上共享相同权重；（2）F-CNNs仅能通过局部感受野获取周围信息。为解决上述问题，我们提出一种基于注意力机制的新型分割框架——MFA-Net（多尺度特征融合注意力网络）。该框架能够学习多尺度间更具意义的特征图，从而实现更精确的自动分割。我们在两个二维肝脏CT数据集上将所提出的MFA-Net与最新方法进行对比。实验结果表明，我们的MFA-Net在不同尺度的图像上均能产生更精确的分割结果。