The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.
Journal:
Technology in cancer research & treatment
Published Date:
Jan 1, 2019
Abstract
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience of the oncologist. Using deep learning methods to automate target delineation may increase its efficiency. We used a modified deep learning model called U-Net to automatically segment and delineate tumor targets in patients with nasopharyngeal carcinoma. Patients were randomly divided into a training set (302 patients), validation set (100 patients), and test set (100 patients). The U-Net model was trained using labeled computed tomography images from the training set. The U-Net was able to delineate nasopharyngeal carcinoma tumors with an overall dice similarity coefficient of 65.86% for lymph nodes and 74.00% for primary tumor, with respective Hausdorff distances of 32.10 and 12.85 mm. Delineation accuracy decreased with increasing cancer stage. Automatic delineation took approximately 2.6 hours, compared to 3 hours, using an entirely manual procedure. Deep learning models can therefore improve accuracy, consistency, and efficiency of target delineation in T stage, but additional physician input may be required for lymph nodes.
Authors
Keywords
Adolescent
Adult
Aged
Algorithms
Deep Learning
Female
Humans
Image Processing, Computer-Assisted
Male
Middle Aged
Models, Theoretical
Nasopharyngeal Neoplasms
Neoplasm Grading
Neoplasm Staging
Organs at Risk
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Image-Guided
Tomography, X-Ray Computed
Young Adult