Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (4): 1183-1188.DOI: 10.11772/j.issn.1001-9081.2018091908

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Automatic segmentation of nasopharyngeal neoplasm in MR image based on U-net model

PAN Peike1, WANG Yan2, LUO Yong3, ZHOU Jiliu2   

  1. 1. College of Electronics and Information Engineering, Sichuan University, Chengdu Sichuan 610065, China;
    2. College of Computer Science, Sichuan University, Chengdu Sichuan 610065, China;
    3. Department of Oncology, West China Hospital, Sichuan University, Chengdu Sichuan 610041, China
  • Received:2018-09-13 Revised:2018-11-01 Online:2019-04-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61701324).


潘沛克1, 王艳2, 罗勇3, 周激流2   

  1. 1. 四川大学 电子信息学院, 成都 610065;
    2. 四川大学 计算机学院, 成都 610065;
    3. 四川大学 华西医院肿瘤科, 成都 610041
  • 通讯作者: 王艳
  • 作者简介:潘沛克(1995-),男,湖北鄂州人,硕士研究生,主要研究方向:深度学习、医学图像处理;王艳(1986-),女,宁夏平罗人,讲师,博士,主要研究方向:医学图像处理、模式识别;罗勇(1980-),男,四川成都人,主治医师,博士,主要研究方向:医学影像分析;周激流(1963-),男,四川成都人,教授,博士,主要研究方向:数字图像处理、分数阶微积分、无线传感网络。
  • 基金资助:

Abstract: Because of the uncertain growth direction and complex anatomical structure for nasopharyngeal tumors, doctors always manually delineate the tumor regions in MR images, which is time-consuming and the delineation result heavily depends on the experience of doctors. In order to solve this problem, based on deep learning algorithm, a U-net based MR image automatic segmentation algorithm of nasopharyngeal tumors was proposed, in which the max-pooling operation in original U-net model was replaced by the convolution operation to keep more feature information. Firstly,the regions of 128×128 were extracted from all slices with tumor regions of the patients as data samples. Secondly, the patient samples were divided into training sample set and testing sample set, and data augmentation was performed on the training samples. Finally, all the training samples were used to train the model. To evaluate the performance of the proposed U-net based model, all slices of patients in testing sample set were selected for segmentation, and the final average results are:Dice Similarity Coefficient (DSC) is 80.05%, Prevent Match (PM) coefficient is 85.7%, Correspondence Ratio (CR) coefficient is 71.26% and Average Symmetric Surface Distance (ASSD) is 1.1568. Compared with Convolutional Neural Network (CNN) based model, DSC, PM and CR coefficients of the proposed method are increased by 9.86 percentage points, 19.61 percentage points and 16.02 percentage points respectively, and ASSD is decreased by 0.4364. Compared with Fully Convolutional Network (FCN) model and max-pooling based U-net model, DSC and CR coefficients of the proposed method achieve the best results, while PM coefficient is 2.55 percentage points lower than the maximum value in the two comparison models, and ASSD is slightly higher than the minimum value of the two comparison models by 0.0046. The experimental results show that the proposed model can achieve good segmentation results of nasopharyngeal neoplasm, which assists doctors in diagnosis.

Key words: nasopharyngeal neoplasm, medical image segmentation, deep learning model, end-to-end model, U-net model

摘要: 鼻咽肿瘤生长方向不确定,解剖结构复杂,当前主要依靠医生手动分割,该方法耗时久同时严重依赖于医生的经验。针对这一问题,基于深度学习理论,提出一种基于U-net模型的全自动鼻咽肿瘤MR图像分割算法,利用卷积操作替换原始U-net模型中的最大池化操作以减少特征信息的损失。首先,从所有患者的肿瘤切片中提取大小为128×128的区域作为数据样本;然后,将患者样本分为训练样本集和测试样本集,并对训练样本集进行数据扩充;最后,选择训练样本集中所有数据用于训练网络模型。为了验证所提模型的有效性,选取测试样本集中患者的所有肿瘤切片进行分割,最终平均分割精度可达到:DSC(Dice Similarity Coefficient)为80.05%,PM系数为85.7%,CR系数为71.26%,ASSD(Average Symmetric Surface Distance)指标为1.1568。与基于图像块的卷积神经网络(CNN)相比,所提算法DSC,PM(Prevent Match)、CR(Correspondence Ratio)系数分别提高了9.86个百分点、19.61个百分点、16.02个百分点,ASSD指标下降了0.4364;与全卷积神经网络(FCN)模型及基于最大池化的U-net网络相比,所提算法的DSC、CR系数均取得了最优结果,PM系数较两种对比模型中的最大值低2.55个百分点,ASSD指标较两种对比模型中的最小值略高出0.0046。实验结果表明,所提算法针对鼻咽肿瘤图像可以实现较好的自动化分割效果以辅助医生进行诊断。

关键词: 鼻咽肿瘤, 医学图像分割, 深度学习模型, 端到端模型, U-net模型

CLC Number: