基于HC-CFCN模型的肝脏CT图像分割

doi:10.19678/j.issn.1000-3428.0053712

摘要/Abstract

摘要： 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。

关键词: 肝脏图像分割, 级联式全卷积神经网络, 层间上下文信息, 能量图, 计算机断层扫描

Abstract: 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.

Key words: liver image segmentation, Cascaded Fully Convolutional Network(CFCN), hierarchical contextual information, energy image, Computed Tomography(CT)

中图分类号:

TP391

刘天宇, 姜威威, 何江萍, 韩金仓. 基于HC-CFCN模型的肝脏CT图像分割[J]. 计算机工程, 2020, 46(2): 268-273.

LIU Tianyu, JIANG Weiwei, HE Jiangping, HAN Jincang. Segmentation of Liver CT Images Based on HC-CFCN Model[J]. Computer Engineering, 2020, 46(2): 268-273.

https://www.ecice06.com/CN/Y2020/V46/I2/268

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