Evolution Model Based on Prior Information for Narrow Joint Segmentation

Xin Wang; Shuai Xu; Zhen Ye; Chao Zheng Zhou; Jing Qin

doi:10.1007/s40305-019-00265-w

Evolution Model Based on Prior Information for Narrow Joint Segmentation

Xin Wang
, Shuai Xu
, Zhen Ye
, Chao Zheng Zhou
, Jing Qin

Producción científica: Article › revisión exhaustiva

Resumen

Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

Idioma original	English
Páginas (desde-hasta)	629-642
Número de páginas	14
Publicación	Journal of the Operations Research Society of China
Volumen	7
N.º	4
DOI	https://doi.org/10.1007/s40305-019-00265-w
Estado	Published - dic 1 2019

Nota bibliográfica

Publisher Copyright:
© 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.

Financiación

This research was supported in part by the National Natural Science Foundation of China (Nos. 11771276, 11471208) and the capacity construction project of local universities in Shanghai (No. 18010500600). The research of Jing Qin was supported by the National Science Foundation of USA (No. DMS-1941197)

Financiadores	Número del financiador
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China	1941197, DMS-1818374
National Natural Science Foundation of China (NSFC)	11771276, 18010500600, 11471208

ASJC Scopus subject areas

General Mathematics
Management Science and Operations Research

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title = "Evolution Model Based on Prior Information for Narrow Joint Segmentation",

abstract = "Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.",

keywords = "CT, Feature prior, Hip joint, Image segmentation, Level set, Variational model",

author = "Xin Wang and Shuai Xu and Zhen Ye and Zhou, \{Chao Zheng\} and Jing Qin",

note = "Publisher Copyright: {\textcopyright} 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.",

year = "2019",

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AU - Wang, Xin

AU - Xu, Shuai

AU - Ye, Zhen

AU - Zhou, Chao Zheng

AU - Qin, Jing

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

AB - Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

KW - CT

KW - Feature prior

KW - Hip joint

KW - Image segmentation

KW - Level set

KW - Variational model

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JO - Journal of the Operations Research Society of China

JF - Journal of the Operations Research Society of China

IS - 4

ER -

Evolution Model Based on Prior Information for Narrow Joint Segmentation

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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