Automatic cystocele severity grading in ultrasound by spatio-temporal regression

Dong Ni; Xing Ji; Yaozong Gao; Jie Zhi Cheng; Huifang Wang; Jing Qin; Baiying Lei; Tianfu Wang; Guorong Wu; Dinggang Shen

doi:10.1007/978-3-319-46723-8_29

Automatic cystocele severity grading in ultrasound by spatio-temporal regression

Dong Ni, Xing Ji, Yaozong Gao, Jie Zhi Cheng, Huifang Wang, Jing Qin, Baiying Lei, Tianfu Wang, Guorong Wu, Dinggang Shen

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However,this process is time-consuming and operator-dependent. In this study,we propose an automatic scheme for csystocele grading from transperineal US video. A two-layer spatio-temporal regression model is proposed to identify the middle axis and lower tip of the SP,and segment the bladder,which are essential tasks for the measurement of the MDB. Both appearance and context features are extracted in the spatio-temporal domain to help the anatomy detection. Experimental results on 85 transperineal US videos show that our method significantly outperforms the state-of-theart regression method.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Publisher	Springer Verlag
Pages	247-255
Number of pages	9
ISBN (Print)	9783319467221
DOIs	https://doi.org/10.1007/978-3-319-46723-8_29
Publication status	Published - 1 Jan 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9901 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Cystocele
Regression
Spatio-temporal
Ultrasound

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

More information

10.1007/978-3-319-46723-8_29

Cite this

Ni, D., Ji, X., Gao, Y., Cheng, J. Z., Wang, H., Qin, J., Lei, B., Wang, T., Wu, G., & Shen, D. (2016). Automatic cystocele severity grading in ultrasound by spatio-temporal regression. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 247-255). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_29

Ni, Dong ; Ji, Xing ; Gao, Yaozong et al. / Automatic cystocele severity grading in ultrasound by spatio-temporal regression. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag, 2016. pp. 247-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However,this process is time-consuming and operator-dependent. In this study,we propose an automatic scheme for csystocele grading from transperineal US video. A two-layer spatio-temporal regression model is proposed to identify the middle axis and lower tip of the SP,and segment the bladder,which are essential tasks for the measurement of the MDB. Both appearance and context features are extracted in the spatio-temporal domain to help the anatomy detection. Experimental results on 85 transperineal US videos show that our method significantly outperforms the state-of-theart regression method.",

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Ni, D, Ji, X, Gao, Y, Cheng, JZ, Wang, H, Qin, J, Lei, B, Wang, T, Wu, G & Shen, D 2016, Automatic cystocele severity grading in ultrasound by spatio-temporal regression. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, Springer Verlag, pp. 247-255. https://doi.org/10.1007/978-3-319-46723-8_29

Automatic cystocele severity grading in ultrasound by spatio-temporal regression. / Ni, Dong; Ji, Xing; Gao, Yaozong et al.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag, 2016. p. 247-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Ni, Dong

AU - Ji, Xing

AU - Gao, Yaozong

AU - Cheng, Jie Zhi

AU - Wang, Huifang

AU - Qin, Jing

AU - Lei, Baiying

AU - Wang, Tianfu

AU - Wu, Guorong

AU - Shen, Dinggang

PY - 2016/1/1

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N2 - Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However,this process is time-consuming and operator-dependent. In this study,we propose an automatic scheme for csystocele grading from transperineal US video. A two-layer spatio-temporal regression model is proposed to identify the middle axis and lower tip of the SP,and segment the bladder,which are essential tasks for the measurement of the MDB. Both appearance and context features are extracted in the spatio-temporal domain to help the anatomy detection. Experimental results on 85 transperineal US videos show that our method significantly outperforms the state-of-theart regression method.

AB - Cystocele is a common disease in woman. Accurate assessment of cystocele severity is very important for treatment options. The transperineal ultrasound (US) has recently emerged as an alternative tool for cystocele grading. The cystocele severity is usually evaluated with the manual measurement of the maximal descent of the bladder (MDB) relative to the symphysis pubis (SP) during Valsalva maneuver. However,this process is time-consuming and operator-dependent. In this study,we propose an automatic scheme for csystocele grading from transperineal US video. A two-layer spatio-temporal regression model is proposed to identify the middle axis and lower tip of the SP,and segment the bladder,which are essential tasks for the measurement of the MDB. Both appearance and context features are extracted in the spatio-temporal domain to help the anatomy detection. Experimental results on 85 transperineal US videos show that our method significantly outperforms the state-of-theart regression method.

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EP - 255

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

PB - Springer Verlag

ER -

Ni D, Ji X, Gao Y, Cheng JZ, Wang H, Qin J et al. Automatic cystocele severity grading in ultrasound by spatio-temporal regression. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 247-255. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46723-8_29

Automatic cystocele severity grading in ultrasound by spatio-temporal regression

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