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• 图像处理与计算机视觉 • 上一篇    下一篇

一种改进FCN 的肝脏肿瘤CT 图像分割方法

  

  1. 1. 苏州科技大学电子与信息工程学院，江苏 苏州 215009；
    2. 苏州市虚拟现实智能交互及应用技术重点实验室，江苏 苏州 215009
  • 出版日期:2020-02-29 发布日期:2020-03-11
  • 基金资助:
    国家自然科学基金项目(61876217)；苏州市科技发展计划项目(SYG201707)

A CT image segmentation method for liver tumor by an improved FCN

  1. 1. College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou Jiangsu 215009, China;
    2. Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou Jiangsu 215009, China
  • Online:2020-02-29 Published:2020-03-11

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摘要： 精准的医学图像分割是辅助疾病诊断和手术规划的必要步骤。由于腹部器官边界
模糊、对比度不高，肝脏肿瘤的自动分割一直是一个难题。针对传统全卷积神经网络(FCN)实
现端到端分割精度不佳等问题，提出了一种卷积型多尺度融合FCN 的CT 图像肝脏肿瘤分割方
法。首先，通过提高对比度、增强和去噪的方式对原始的CT 图像数据集进行预处理；然后使
用处理后的数据集对所设计好的FCN 网络进行训练；最终得出能够精确分割肝脏肿瘤的网络模
型。实验效果采用多种评价指标进行分割结果的评估，并且与多种常见的分割网络进行对比。
实验结果表明本文方法可以精准分割CT 图像中各种形状和大小的肝脏肿瘤，分割效果良好，
能够为临床的诊断提供可靠的依据。

关键词: 全卷积神经网络, 肝脏肿瘤分割, 深度学习, 图像分割, 卷积多尺度融合

Abstract: Accurate medical image segmentation is a necessary step in assisting disease diagnosis and
surgical planning. The automatic segmentation of liver tumors has always been a difficult problem
due to the blurred borders and the low contrast of abdominal organs. Aiming at the problem that the
traditional full convolutional network (FCN) achieves low accuracy in end-to-end segmentation, this
paper proposes a CT image liver tumor segmentation method based on convolutional multi-scale
fusion FCN. Firstly, the original CT image dataset is preprocessed by improving the contrast,
enhancement and denoising. Secondly, the designed FCN network is trained using the processed
dataset. Finally, a network model capable of accurately segmenting the liver tumor is obtained. The
experiment adopts a variety of evaluation indicators to evaluate the effectiveness of segmentation
results and makes comparison with a variety of common segmentation networks. The experimental
results show that the method proposed in this paper can accurately segment liver tumors of various
shapes and sizes in CT images, and the segmentation effect is good, which can provide a reliable
support for clinical diagnosis.

Key words: full convolutional network, liver tumor segmentation, deep learning, image segmentation;
convolution multi-scale fusion