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.
Highlights
Nasopharyngeal cancer is a malignant tumor, and the clinical manifestations of patients are mainly nasal congestion, blood in the nose, and hearing loss, which seriously endanger patients’ life and health
E segmentation results of a randomly selected batch of experiments (Figure 7) are presented, revealing the performance of the proposed network in this paper. e average segmentation time for automatic segmentation of organs at risk (OARs) in CT images of 20 test set patients using the an improved network based on 3D Unet (AUnet) study model is about 13 seconds, which is a great improvement in efficiency over manual outlining
One of the fold cross-validation results is shown in Figure 8. e results showed that the loss function gradually stabilized after about 250 epochs. ere was no statistical difference between the automatic segmentation results of AUnet and the manual segmentation results of physicians (p > 0.05)
Summary
Nasopharyngeal cancer is a malignant tumor, and the clinical manifestations of patients are mainly nasal congestion, blood in the nose, and hearing loss, which seriously endanger patients’ life and health. The common clinical treatment for nasopharyngeal carcinoma is radiation therapy, which has certain moderate sensitivity [1]. It is important to give patients the corresponding rehabilitation exercise [2]. Erefore, in this study, radiation therapy combined with temporomandibular joint exercise intervention was used for patients with nasopharyngeal carcinoma, with the aim of investigating the effect of radiation therapy and magnetic resonance imaging (MRI) signs. Radiotherapy is the only treatment mode that can be electronic and intelligent, and it is very important to promote the development of intelligent technology of radiotherapy to improve the efficacy of tumor patients. It is very important to promote the development of intelligent radiotherapy technology to improve the efficacy of tumor patients. With the development of computer technology and intensity modulation technology, intensity modulated radiotherapy (IM-RT) uses pen-shaped beams of different intensities to irradiate the tumor target area and adjacent important tissues at different doses and precisely outlines the tumor target area and organs at risk (OARs). e
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
