Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.
Bibliographical noteFunding Information:
Manuscript received December 8, 2016; revised June 19, 2017 and October 13, 2017; accepted October 26, 2017. Date of publication November 1, 2017; date of current version November 28, 2017. This work was supported in part by the grants from China Scholarship Council, in part by the National Basic Research Program of China (973 Program) under Grant 2010CB732501, in part by NIH under Grant CA206100, in part by the National Natural Science Foundation of China under Grant 61701117, and in part by the Natural Science Foundation of Fujian Province under Grant 2017J01736. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Oleg V. Michailovich. (Corresponding author: Dinggang Shen.) Z. Wang is with the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China, and also with the Biomedical Research Imaging Center, Department of Radiology, University of North Carolina, Chapel Hill, NC 27599 USA (e-mail: email@example.com).
© 1992-2012 IEEE.
- Image segmentation
- head and neck cancer
- machine learning
- radiotherapy planning
- random forest
- vertex regression
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design