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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2768621
|2 doi
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|a pubmed24n0946.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Zhensong Wang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning
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| 264 |
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|c 2018
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 11.12.2018
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| 500 |
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|a Date Revised 12.11.2023
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| 500 |
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|a published: Print
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|a Citation Status MEDLINE
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|a 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
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Lifang Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li Wang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yaozong Gao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wufan Chen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dinggang Shen
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 2 vom: 14. Feb., Seite 923-937
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|g volume:27
|g year:2018
|g number:2
|g day:14
|g month:02
|g pages:923-937
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|u http://dx.doi.org/10.1109/TIP.2017.2768621
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