Abstract

Info

Title:: Automatic optic chiasm segmentation using CT and MRI based on cascaded 3D U-Net

Author(s):: SHEN Zhenjiong1; PENG Zhao1; MENG Xiangyin1; WANG Zhi1; 2; XU Xie1; 3; PEI Xi 1; 4; 1. Institute of Nuclear Medical Physics, University of Science and Technology of China, Hefei 230025, China 2. Department of Radiation Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China 3. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China 4. Anhui Wisdom Technology Co., Ltd, Hefei 230088, China

Keywords:: Keywords: 3D U-Net optic chiasm automatic segmentation multimodal

Abstract:: Abstract: Objective To realize the automatic segmentation of the optic chiasm using multimodal images (CT and MRI) that contain head-and-neck data and based on cascaded 3D U-Net for obtaining a higher segmentation accuracy than using only CT data. Methods The proposed cascaded 3D U-Net consists of an original 3D U-Net and an improved 3D D-S U-Net (3D Deeply-Supervised U-Net). The head-and-neck CT images and MRI images (T1 and T2 modalities) of 60 patients were used in the experiment, and the data of 15 patient were randomly selected as the test set. Dice similarity coefficient was used to evaluate the accuracy of automatic optic chiasm segmentation. Results For all cases in the test set, the Dice similarity coefficient of the optic chiasm segmentation using multimodal data (CT and MRI) or monomodal data (CT) was 0.645±0.085 and 0.552 ±0.096, respectively. Conclusion The multimodal automatic segmentation model based on cascaded 3D U-Net can accurately realize the automatic segmentation of the optic chiasm, superior to the method using only monomodal data, and it can assist doctors in improving the efficiency of radiotherapy planning.