title: Interactive contour delineation and refinement in treatment planning of image-guided radiation therapy.
abstract: The accurate contour delineation of the target and/or organs at risk (OAR) is essential in treatment planning for image-guided radiation therapy (IGRT). Although many automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. Moreover, clinicians would like to analyze the characteristics of regions of interests (ROI) and adjust contours manually during IGRT. Interactive tool for contour delineation is necessary in such cases. In this work, a novel approach of curve fitting for interactive contour delineation is proposed. It allows users to quickly improve contours by a simple mouse click. Initially, a region which contains interesting object is selected in the image, then the program can automatically select important control points from the region boundary, and the method of Hermite cubic curves is used to fit the control points. Hence, the optimized curve can be revised by moving its control points interactively. Meanwhile, several curve fitting methods are presented for the comparison. Finally, in order to improve the accuracy of contour delineation, the process of the curve refinement based on the maximum gradient magnitude is proposed. All the points on the curve are revised automatically towards the positions with maximum gradient magnitude. Experimental results show that Hermite cubic curves and the curve refinement based on the maximum gradient magnitude possess superior performance on the proposed platform in terms of accuracy, robustness, and time calculation. Experimental results of real medical images demonstrate the efficiency, accuracy, and robustness of the proposed process in clinical applications.
authors.affiliation: Shenzhen Institutes of Advanced Technology. wu.zhouo@siat.ac.cn.
authors.forename: Wu
authors.initials: W
authors.lastname: Zhou
authors.forename: Yaoqin
authors.initials: Y
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D008279:Magnetic Resonance Imaging; D051379:Mice; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted; D061089:Radiotherapy, Image-Guided; D014057:Tomography, X-Ray Computed