Application of Artificial Intelligence in Radiotherapy of Nasopharyngeal Carcinoma with Magnetic Resonance Imaging

Abstract

The value of automatic organ-at-risk outlining software for radiotherapy is based on artificial intelligence technology in clinical applications. The accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma was investigated. In the automatic segmentation model which is proposed in this paper, after CT scans and manual segmentation by physicians, CT images of 147 nasopharyngeal cancer patients and their corresponding outlined OARs structures were selected and grouped into a training set (115 cases), a validation set (12 cases), and a test set (20 cases) by complete randomization. Adaptive histogram equalization is used to preprocess the CT images. End-to-end training is utilized to improve modeling efficiency and an improved network based on 3D Unet (AUnet) is implemented to introduce organ size as prior knowledge into the convolutional kernel size design to enable the network to adaptively extract features from organs of different sizes, thus improving the performance of the model. The DSC (Dice Similarity Coefficient) coefficients and Hausdorff (HD) distances of automatic and manual segmentation are compared to verify the effectiveness of the AUnet network. The mean DSC and HD of the test set were 0.86 ± 0.02 and 4.0 ± 2.0 mm, respectively. Except for optic nerve and optic cross, there was no statistical difference between AUnet and manual segmentation results (P > 0.05). With the introduction of the adaptive mechanism, AUnet can achieve automatic segmentation of the endangered organs of nasopharyngeal carcinoma based on CT images more accurately, which can substantially improve the efficiency and consistency of segmentation of doctors in clinical applications.