Abstract

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Title:: Auto-segmentation of glioma based on 3D convolutional network and multimodal MRI

Author(s):: WANG Rui1; QI Chong1; MENG Lanxi1; LIU Zhiqiang2; LI Shaowu1; 1. Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China 2. National Cancer Center/National Clinical Research Center for Cancer/Department of radiotherapy, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

Keywords:: Keywords: glioma automatic segmentation 3D convolutional network multimodal MRI

Abstract:: Abstract: Objective To realize the automatic segmentation of glioma based on 3D convolutional neural network and multimodal magnetic resonance imaging (MRI). Methods The 4-modal MRI data of 369 cases of gliomas from the BRATS2020 public dataset were preprocessed by 3D clipping, resampling, artifacts removal and normalization. The preprocessed MRI data and label information of glioma were input into 3D convolutional neural network based on U-net for training and testing. The segmentation results of the whole tumor region, the core tumor region and the enhanced tumor region were evaluated using Dice similarity coefficient (DSC), recall rate and precision rate. Results For the 74 cases in testing dataset, the mean DSC of the whole tumor region, the core tumor region and the enhanced tumor region were 0.88, 0.77 and 0.73, respectively, and the median values were 0.90, 0.84 and 0.81, respectively. The mean recall rates were 0.88, 0.78 and 0.78, respectively, and the median values were 0.90, 0.84 and 0.84, respectively. The mean precision rates were 0.89, 0.83 and 0.75, respectively, and the median values were 0.91, 0.89 and 0.79, respectively. Conclusion The 3D convolutional neural network based on U-net achieves good segmentation results on multimodal MRI dataset, showing its potential for automatic segmentation of glioma, and it would be beneficial for clinical use in diagnosis and treatment planning.