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Automatic Cystocele Severity Grading in Ultrasound by Spatio-Temporal Regression

  • Dong NiEmail author
  • Xing Ji
  • Yaozong Gao
  • Jie-Zhi Cheng
  • Huifang Wang
  • Jing Qin
  • Baiying Lei
  • Tianfu Wang
  • Guorong Wu
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

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-the-art regression method.

Keywords

Ultrasound Regression Spatio-temporal Cystocele 

Notes

Acknowledgement

This work was supported by the National Natural Science Funds of China (Nos. 61501305, 61571304, and 81571758), the Shenzhen Basic Research Project (Nos. JCYJ20150525092940982 and JCYJ20140509172609164), and the Natural Science Foundation of SZU (No. 2016089).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dong Ni
    • 1
    Email author
  • Xing Ji
    • 1
  • Yaozong Gao
    • 2
  • Jie-Zhi Cheng
    • 1
  • Huifang Wang
    • 3
  • Jing Qin
    • 4
  • Baiying Lei
    • 1
  • Tianfu Wang
    • 1
  • Guorong Wu
    • 2
  • Dinggang Shen
    • 2
  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical EngineeringShenzhen UniversityShenzhenChina
  2. 2.Department of Radiology and BRICUNC at Chapel HillChapel HillUSA
  3. 3.Department of UltrasoundShenzhen Second Peoples HospitalShenzhenChina
  4. 4.School of Nursing, Centre for Smart HealthThe Hong Kong Polytechnic UniversityKowloonHong Kong

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