Hierarchical Vertex Regression Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

Zhensong Wang, Lifang Wei, Li Wang, Yaozong Gao, Wufan Chen, Dinggang Shen

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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. 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.

Original languageEnglish
JournalIEEE Transactions on Image Processing
DOIs
Publication statusAccepted/In press - 2017 Oct 31

Fingerprint

Radiotherapy
Organs at Risk
Neck
Head
Planning
Learning
Intensity-Modulated Radiotherapy
Tissue
Head and Neck Neoplasms
Artifacts
Joints
Therapeutics

Keywords

  • Biomedical imaging
  • Computed tomography
  • Deformable models
  • Image segmentation
  • Shape
  • Testing
  • Training

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this

Hierarchical Vertex Regression Based Segmentation of Head and Neck CT Images for Radiotherapy Planning. / Wang, Zhensong; Wei, Lifang; Wang, Li; Gao, Yaozong; Chen, Wufan; Shen, Dinggang.

In: IEEE Transactions on Image Processing, 31.10.2017.

Research output: Contribution to journalArticle

@article{72692785b38e4fc38d93c9ec7685b30d,
title = "Hierarchical Vertex Regression Based Segmentation of Head and Neck CT Images for Radiotherapy Planning",
abstract = "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. 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.",
keywords = "Biomedical imaging, Computed tomography, Deformable models, Image segmentation, Shape, Testing, Training",
author = "Zhensong Wang and Lifang Wei and Li Wang and Yaozong Gao and Wufan Chen and Dinggang Shen",
year = "2017",
month = "10",
day = "31",
doi = "10.1109/TIP.2017.2768621",
language = "English",
journal = "IEEE Transactions on Image Processing",
issn = "1057-7149",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Hierarchical Vertex Regression Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

AU - Wang, Zhensong

AU - Wei, Lifang

AU - Wang, Li

AU - Gao, Yaozong

AU - Chen, Wufan

AU - Shen, Dinggang

PY - 2017/10/31

Y1 - 2017/10/31

N2 - 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. 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.

AB - 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. 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.

KW - Biomedical imaging

KW - Computed tomography

KW - Deformable models

KW - Image segmentation

KW - Shape

KW - Testing

KW - Training

UR - http://www.scopus.com/inward/record.url?scp=85032801968&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032801968&partnerID=8YFLogxK

U2 - 10.1109/TIP.2017.2768621

DO - 10.1109/TIP.2017.2768621

M3 - Article

C2 - 29757737

AN - SCOPUS:85032801968

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

SN - 1057-7149

ER -