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Title: Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

Abstract

Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-basedmore » image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments.« less

Authors:
 [1];  [2];  [2];  [2]
  1. Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI (United States). E-mail: tiezhi.zhang@beaumont.edu
  2. Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI (United States)
Publication Date:
OSTI Identifier:
20951674
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 68; Journal Issue: 2; Other Information: DOI: 10.1016/j.ijrobp.2007.01.038; PII: S0360-3016(07)00199-X; Copyright (c) 2007 Elsevier Science B.V., Amsterdam, Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; COMPUTERIZED TOMOGRAPHY; HEAD; IMAGES; MANUALS; NECK; PATIENTS; RADIOTHERAPY

Citation Formats

Zhang Tiezhi, Chi Yuwei, Meldolesi, Elisa, and Yan Di. Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy. United States: N. p., 2007. Web. doi:10.1016/j.ijrobp.2007.01.038.
Zhang Tiezhi, Chi Yuwei, Meldolesi, Elisa, & Yan Di. Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy. United States. doi:10.1016/j.ijrobp.2007.01.038.
Zhang Tiezhi, Chi Yuwei, Meldolesi, Elisa, and Yan Di. Fri . "Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy". United States. doi:10.1016/j.ijrobp.2007.01.038.
@article{osti_20951674,
title = {Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy},
author = {Zhang Tiezhi and Chi Yuwei and Meldolesi, Elisa and Yan Di},
abstractNote = {Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments.},
doi = {10.1016/j.ijrobp.2007.01.038},
journal = {International Journal of Radiation Oncology, Biology and Physics},
number = 2,
volume = 68,
place = {United States},
year = {Fri Jun 01 00:00:00 EDT 2007},
month = {Fri Jun 01 00:00:00 EDT 2007}
}