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Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net
Author(s): Zhe Guo; Ning Guo; Kuang Gong; Quanzheng Li
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Paper 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.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095009 (13 March 2019); doi: 10.1117/12.2513229
Show Author Affiliations
Zhe Guo, Massachusetts General Hospital (United States)
Beijing Institute of Technology (China)
Ning Guo, Massachusetts General Hospital (United States)
Kuang Gong, Massachusetts General Hospital (United States)
Quanzheng Li, Massachusetts General Hospital (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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