基于残差双注意力U-Net模型的CT图像囊肿肾脏自动分割

人体肾脏存在形状的多样性和解剖学的复杂性，囊肿病变也会导致肾脏形状发生大幅变化。为应对CT图像囊肿肾脏自动分割存在的诸多挑战，提出一种新型深度分割网络模型。该模型设计有带残差连接的双注意力模块，在残差结构的基础上，联合空间注意力和通道注意力机制自适应学习更加有效的特征表达。依据U-Net架构，以残差双注意力模块为基础模块构建编码器和解码器，设置层级间的跳跃连接，使网络能够更加关注肾脏区域特征，有效应对肾脏的形状变化。为了验证所提模型的有效性，从医院共采集79位肾囊肿患者的CT图像进行训练和测试，实验结果表明该模型能够准确分割CT图像切片中的肾脏区域，且各项分割指标优于多个经典分割网络模型。

Automated segmentation of cystic kidney in CT images using residual double attention motivated U-Net model

Human kidneys have a variety of shapes and anatomical complexity. Cyst lesions can also cause large changes in kidney shape. This paper proposed a new deep network segmentation model to cope with the many challenges of automatic segmentation of CT image cysts. The proposed model deployed a dual attention module with residual connection. Based on the residual structure, it adopted the joint spatial attention and channel attention mechanism to learn more effective feature expression. According to the U-Net architecture, it built the encoder and decoder with the residual dual attention module as the building block, and also set the jump connections between the layers, so that the network could pay more attention to the characteristics of the kidney region and cope well with the changes in kidney shape. In order to verify the validity of the proposed model, it collected CT images of 79 patients with renal cysts from the hospital for training and testing. The experimental results show that the model can accurately segment the kidney regions in CT image slices, and the segmentation indicators are better than some classic segmentation network models.