Esophagus segmentation from 3D CT data using skeleton prior-based graph cut.
The segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper, we focus on esophagus segmentation, a challenging application since the wall of the esophagus, made of muscle tissue, has very low contrast in CT images. We propose in this paper an original method to segment in thoracic CT scans the 3D esophagus using a skeleton-shape model to guide the segmentation. Our method is composed of two steps: a 3D segmentation by graph cut with skeleton prior, followed by a 2D propagation. Our method yields encouraging results over 6 patients.
Université de Rouen, LITIS EA 4108, 22 Boulevard Gambetta, 76183 Rouen Cedex, France.
Damien
D
Grosgeorge
Caroline
C
Petitjean
Bernard
B
Dubray
Su
S
Ruan
D000465:Algorithms; D003198:Computer Simulation; D004947:Esophagus / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008223:Lymphoma / Q000000981:diagnostic imaging / Q000532:radiotherapy; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000706:statistics & numerical data; D011880:Radiotherapy Planning, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*