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 proceedingConference article published in proceeding or bookAcademic researchpeer-review


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 languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467221
Publication statusPublished - 1 Jan 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Cystocele
  • Regression
  • Spatio-temporal
  • Ultrasound

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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