Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy
Hai Hu, Qiang Yang, Jie Li, Pei Wang, Bin Tang, Xianliang Wang, Jinyi Lang
Abstract
PURPOSE: Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning. MATERIAL AND METHODS: percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared. RESULTS: at a scenario of doubled maximum shaft error. CONCLUSIONS: We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.