SU-GG-T-477: Influence of Pitch and Jaw Width On Helical Tomotherapy Head and Neck Treatment Planning

2008 ◽  
Vol 35 (6Part16) ◽  
pp. 2834-2835
Author(s):  
M Moldovan ◽  
T Lee ◽  
C Chu ◽  
K Hogstrom ◽  
R Fields ◽  
...  
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Helical Tomotherapy

scholarly journals PO-0673: Accelerated Helical Tomotherapy versus RapidArc in a head and neck cancer treatment planning study

2013 ◽  
Vol 106 ◽  
pp. S258
Author(s):  
D. Van Gestel ◽  
G. De Kerf ◽  
W. Crijns ◽  
F. Van den Heuvel ◽  
B. De Ost ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Cancer Treatment ◽  
Head And Neck ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Helical Tomotherapy ◽  
Planning Study

Utility of Megavoltage Fan-Beam CT for Treatment Planning in a Head-And-Neck Cancer Patient with Extensive Dental Fillings Undergoing Helical Tomotherapy

2010 ◽  
Vol 35 (2) ◽  
pp. 108-114 ◽  
Author(s):  
Claus Yang ◽  
Tianxiao Liu ◽  
Richard L. Jennelle ◽  
Janice K. Ryu ◽  
Srinivasan Vijayakumar ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Cancer Patient ◽  
Head And Neck ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Helical Tomotherapy ◽  
Neck Cancer Patient ◽  
Dental Fillings

2082 A pilot study on the use of volume visualization in image-based treatment planning for head and neck cancer

1995 ◽  
Vol 32 ◽  
pp. 302
Author(s):  
L. Scott Johnson ◽  
Charles A. Pelizzari ◽  
Robert Grzeszczuk ◽  
Martin Ryan ◽  
Daniel J. Haraf ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Pilot Study ◽  
Head And Neck ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Volume Visualization

Parotid Gland Sparing With Helical Tomotherapy in Head-and-Neck Cancer

2012 ◽  
Vol 84 (2) ◽  
pp. 443-448 ◽  
Author(s):  
Mia Voordeckers ◽  
Ashraf Farrag ◽  
Hendrik Everaert ◽  
Koen Tournel ◽  
Guy Storme ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Parotid Gland ◽  
Head And Neck ◽  
Neck Cancer ◽  
Helical Tomotherapy

Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

2022 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
Junqian Zhang ◽  
Yingming Sun ◽  
Hongen Liao ◽  
Jian Zhu ◽  
Yuan Zhang
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Helical Tomotherapy ◽  
Radiation Treatment ◽  
Automatic Segmentation ◽  
Parotid Glands ◽  
Deep Convolutional Neural Networks ◽  
Megavoltage Computed Tomography ◽  
Radiation Induced ◽  
Segmentation Models

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.

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