基于HC-CFCN模型的肝脏CT图像分割

在计算机断层扫描(CT)图像中肝脏与相邻器官灰度值近似,且不同患者的肝脏轮廓存在差异性,导致肝脏CT图像的精确分割成为医学图像处理中的难题之一。为实现肝脏CT图像的自动分割,构建一种层间上下文级联式的全卷积神经网络模型HC-CFCN。利用第1级网络实现肝脏轮廓的粗略分割,并将其分割结果与原始CT图像、肝脏能量图共同作为第2级网络的输入,优化分割结果。在LiTS数据集上的实验结果表明,与U-Net、FCN+3DCRF和V-Net模型相比,HC-CFCN模型的分割精度较高。

Segmentation of Liver CT Images Based on HC-CFCN Model

Livers have similar gray values to surrounding organs in Computed Tomography(CT)images,and the shape of a liver varies among different patients,making precise segmentation of liver CT images a hard problem in medical image processing.To address the problem,this paper proposes a Hierarchical Contextual Cascaded Fully Convolutional Network(HC-CFCN)model to implement automated segmentation of liver CT images.The first-level network is used to realize rough segmentation of the liver contour,and the segmentation results are used as the input of the second-level network together with the original CT image and liver energy image to optimize the segmentation results.Experimental results on the LiTS dataset show that the HC-CFCN model has a higher segmentation precision than U-Net,FCN+3DCRF and V-Net models.