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MO‐E‐330A‐07: Knowledge‐Based Auto‐Contouring in 4D Radiation Therapy
Author(s) -
Chao M,
Schreibmann E,
Li T,
Xing L
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2241455
Subject(s) - contouring , segmentation , computer science , artificial intelligence , computer vision , similarity (geometry) , medical imaging , breathing , image segmentation , pattern recognition (psychology) , medicine , image (mathematics) , computer graphics (images) , anatomy
Purpose: In this work we develop a strategy of automatic contouring to relieve the effort of organ segmentation in 4D radiation therapy. The method adopts a novel technique of control volumes to achieve robust contour mapping among a series of 4D CT images. Methods and Materials: For a given patient, segmentation of tumor and sensitive structures was manually performed for one of the breathing phases by a physician. Along the segmented contours a number of small control volumes (∼ 1cm) were selected. To obtain contours on another CT phase we mapped the control volumes collectively to this phase using rigid transformation, which served as a good starting contour for further adjustment. The final positions of mapped control volumes were determined by minimizing the energy function consisting of two terms: intensity similarity between the mapped volumes and the original volumes in the selected phase; elastic potential energy preventing control volumes from movement. The approach was tested with the 4D CT images of 5 lung cancer patients. Results: For the patients the knowledge‐based approach of automatic contouring worked well even for CT images with significant deformations. In the lung case the contours have the average error of less than 2mm and a maximum error of 5mm for noisy anatomical structures. A significant reduction of time compared with manual contouring was achieved. Conclusions: The auto‐mapping of contours in 4D radiation therapy was implemented with control volumes. The method provides an efficient way for 4D segmentation with high accuracy.