Abstract

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Title:: Construction of a hybrid multi-scale neural network for auto-segmentation of the clinical target areas in Graves ophthalmopathy

Author(s):: ZHANG Shengyuan1; 2; HE Yisong3; LUO Yong3; GOU Chengjun2; FU Yuchuan3; WU Zhangwen2; 1. Department of Radiation Oncology, Shanxi Provincial Cancer Hospital, Xian 710061, China 2. Key Laboratory of Radiation Physics and Technology of Ministry of Education/Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, China 3. Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu 610041, China

Keywords:: Keywords: Graves ophthalmopathy hybrid multi-scale neural network image segmentation deep learning

Abstract:: Abstract: Objective To construct a hybrid multi-scale neural network (HMnet) to automatically delineate the clinical target volumes (CTV) for providing a high-precision model for the auto-segmentation in CT images. Methods HMnet which was an end-to-end convolutional neural network used the deep residual network to extract features, carried out feature processing with multi-scale feature fusion module composed of 4 convolution layers of different cores to adapt to the clinical target volumes of different scales, and introduced the attention residual module to enhance the effective features output by the multi-scale feature fusion module. HMnet was trained and tested using the CT data and CTV contours from 117 cases of Graves ophthalmopathy. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (95HD) were selected as evaluation metrics. Results HMnet (DSC=0.874 9, 95HD=2.525 4 mm) outperformed Unet, Vnet and ResAttUnet3D, and the DSC of HMnet was also higher than the average DSC of two delineation results from the same oncologists. Conclusion HMnet can accurately achieve the automated CTV segmentation in Graves ophthalmopathy, and its clinical application can improve the efficiency of oncologists and the consistency of delineation.