计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230400038-6.doi: 10.11896/jsjkx.230400038

• 图像处理&多媒体技术 • 上一篇    下一篇

面向3D肝脏CT图像分割的改进vnet模型

杨舒琪1, 韩俊玲1,2, 康晓东1, 杨靖怡1, 郭洪洋1, 李博3   

  1. 1 天津医科大学医学影像学院 天津 300202
    2 天津医科大学一中心临床学院 天津 300190
    3 天津医科大学三中心临床学院 天津 300170
  • 发布日期:2024-06-06
  • 通讯作者: 康晓东(yqishu@163.com)
  • 作者简介:(yqishu@163.com)
  • 基金资助:
    京津冀协同创新项目(17YEXTZC00020)

Improved vnet Model for 3D Liver CT Image Segmentation

YANG Shuqi1, HAN Junling1,2, KANG Xiaodong1, YANG Jingyi1, GUO Hongyang1, LI Bo3   

  1. 1 School of Medical Image,Tianjin Medical University,Tianjin 300202,China
    2 The First Central Clinical College of Tianjin Medical University,Tianjin 300190,China
    3 The Third Central Clinical College of Tianjin Medical University,Tianjin 300170,China
  • Published:2024-06-06
  • About author:YANG Shuqi,born in 2001.Her main research interests include medical image processing and so on.
    KANG Xiaodong,born in 1964,Ph.D,professor,is a member of CCF(No.11186S).His main research interests include medical image processing and medical information system integration.
  • Supported by:
    Beijing-Tianjin-Hebei Collaborative Innovation Project(17YEXTZC00020).

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摘要： 分割3D医学影像是放疗计划的重要步骤。临床上,计算机断层扫描被广泛应用于肝脏及肝肿瘤的3D 医学影像图像分割。由于肝脏复杂的边缘结构及纹理特征,肝脏分割仍是一项具有挑战性的工作。针对这一问题,提出了一种面向3D肝脏CT图像精准分割的改进vnet模型。首先,将肝脏CT图像进行HU值截断和重采样,以完成三维数据集的预处理;同时,将vnet解码器和编码器中的卷积核替换为SG模块,即逐通道卷积和逐点卷积的组合,以减小网络模型的参数量。与vnet模型进行对比实验,结果表明该模型方法在肝脏分割数据集上的评估结果总体优越,Dice系数为94.93%,比vnet模型提高了3.49%,大大减少了模型的参数量;同时该方法在MSD脾脏分割数据集和新冠肺炎数据集上也表现出良好的鲁棒性并取得了优越的分割结果。

关键词: 图像分割, 肝脏, vnet, CT, 3D

Abstract: Segmentation of 3D medical images is an important step in radiotherapy planning.In clinical practice,computed tomography is widely used for 3D medical image segmentation of the liver and liver tumours.Due to the complex edge structure and texture features of the liver,liver segmentation is still a challenging task.To address this problem,an improved vnet model for accurate segmentation of 3D liver CT images is proposed.Firstly,the liver CT images are truncated and resampled with HU values to complete the preprocessing of the 3D dataset.Meanwhile,the convolution kernel in the vnet decoder and encoder is replaced with an SG module,which is a combination of depthwise convolution and pointwise convolution,to reduce the number of parameters in the network model.Comparative experiments with the vnet model show that the proposed method is generally superior in the evaluation of the liver segmentation dataset,with a Dice coefficient of 94.93%,an improvement of 3.49% over the vnet model,greatly reducing the number of parameters of the model,while the method also shows good robustness and achieves superior segmentation results on the MSD spleen segmentation dataset and COVID-19 dataset.

Key words: Image segmentation, Liver, vnet, CT, 3D

中图分类号: 

  • TP391
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