Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net

Abstract

Accurate delineation of gross tumor volume (GTV) is essential for head and neck cancer radiotherapy. Complexity of morphology and potential image artifacts usually cause inaccurate manual delineation and interobserver variability. Manual delineation is also time consuming. Motivated by the recent success of deep learning methods in natural and medical image processing, we propose an automatic GTV segmentation approach based on 3D-Unet to achieve automatic GTV delineation. One innovative feature of our proposed method is that PET/CT multi-modality images are integrated in the segmentation network. 175 patients are included in this study with manually drawn GTV by physicians. Based on results from 5-fold cross validation, our proposed method achieves a dice loss of 0.82±0.07 which is better than the model using PET image only (0.79±0.09). In conclusion, automatic GTV segmentation is successfully applied to head and neck cancer patients using deep learning network and multi-modality images, which brings unique benefits for radiation therapy planning.