• Fan Tang, Shujun Liang, Tao Zhong, Xia Huang, Xiaogang Deng, Yu Zhang, and Linghong Zhou.
• School of Biomedical Engineering, Southern Medical University, No. 1838 Guangzhou Northern Avenue, Baiyun District, Guangzhou, 510515, Guangdong, China.
• Eur Radiol. 2020 Feb 1; 30 (2): 823-832.

ObjectivesComputed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images.MethodsDFFM is a multi-sequence MRI-guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (n = 24), grade III (n = 18), or grade IV (n = 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis.ResultsDFFM showed a significantly (p < 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (p > 0.01) with difference grades.ConclusionsDFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning.Key Points• A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.

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