TH-D-M100F-08: Development and Evaluation of An Automatic Contour Propagation Method for 4D Radiotherapy

2007 ◽  
Vol 34 (6Part23) ◽  
pp. 2637-2638
Author(s):  
L Lin ◽  
C Shi ◽  
Y Liu ◽  
G Swanson ◽  
N Papanikolaou
Keyword(s):  
Contour Propagation ◽  
Automatic Contour

scholarly journals EP-1905: Feasibility of automatic contour propagation of spinal bone metastases for online MR-Linac treatment

2016 ◽  
Vol 119 ◽  
pp. S903
Author(s):  
G.G. Sikkes ◽  
L.T.C. Meijers ◽  
C.N. Nomden ◽  
A.N.T.J. Kotte ◽  
G.H. Bol ◽  
...  
Keyword(s):  
Bone Metastases ◽  
Spinal Bone Metastases ◽  
Contour Propagation ◽  
Automatic Contour

A Framework for Clinical Validation of Automatic Contour Propagation: Standardizing Geometric and Dosimetric Evaluation

2019 ◽  
Vol 9 (6) ◽  
pp. 448-455 ◽  
Author(s):  
Amy Frederick ◽  
Michael Roumeliotis ◽  
Petra Grendarova ◽  
Peter Craighead ◽  
Tasnima Abedin ◽  
...  
Keyword(s):  
Clinical Validation ◽  
Contour Propagation ◽  
Automatic Contour

scholarly journals OC-0267: Automatic contour propagation of breast and heart for re-planning in breast cancer radiotherapy

2017 ◽  
Vol 123 ◽  
pp. S138
Author(s):  
G.G. Sikkes ◽  
L.T.C. Meijers ◽  
A.N.T.J. Kotte ◽  
I.E. Van Dam ◽  
B. Van Asselen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Radiotherapy ◽  
Breast Cancer Radiotherapy ◽  
Contour Propagation ◽  
Automatic Contour

scholarly journals Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuhan Yang ◽  
Shoujun Zhou ◽  
Peng Shang ◽  
En Qi ◽  
Shibin Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Active Contour ◽  
Ct Images ◽  
Deformable Registration ◽  
Initial Contour ◽  
Ct Image ◽  
Speeded Up Robust Features ◽  
Contour Propagation ◽  
Feature Based ◽  
Automatic Contour

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

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