%0 Journal Article %A LUO Yong %A PAN Peike %A WANG Yan %A ZHOU Jiliu %T Automatic segmentation of nasopharyngeal neoplasm in MR image based on U-net model %D 2019 %R 10.11772/j.issn.1001-9081.2018091908 %J Journal of Computer Applications %P 1183-1188 %V 39 %N 4 %X 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. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018091908