Automatic cystocele severity grading in transperineal ultrasound by random forest regression

Dong Ni, Xing Ji, Min Wu, Wenlei Wang, Xiaoshuang Deng, Zhongyi Hu, Tianfu Wang, Dinggang Shen, Jie Zhi Cheng, Huifang Wang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Cystocele is a woman disease that bladder herniates into vagina. Women with cystocele may have problem in urinating and higher risk of bladder infection. The treatment of cystocele highly depends on the severity. The cystocele severity is usually evaluated with the manual transperineal ultrasound measurement for the maximal distance between the bladder and the lower tip of symphysis pubis in the Valsalva maneuver. To improve the efficiency of the measurement, we propose a fully automatic scheme that can measure the distance between the two anatomic structures in each ultrasound image. The whole measurement scheme is realized with a two-phase random forest regression to infer the locations of the two structures in the images for the support of distance measurement. The experimental results suggest automatic distance measurements and the final grading by our random forest regression method are comparable to the measurements and grading scores from three medical doctors, and thus corroborate the efficacy of our method.

Original languageEnglish
Pages (from-to)551-560
Number of pages10
JournalPattern Recognition
Publication statusPublished - 2017 Mar 1

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61501305 , 61571304 , and 81571758 ), the Shenzhen Basic Research Project (Nos. JCYJ20150525092940982 and JCYJ20140509172609164 ), Shenzhen Health and Family Planning Research Project (No. 201601027), and the Natural Science Foundation of SZU (No. 2016089 ) Shenzhen Basic Research Project JCYJ20150525092940982.


  • Auto-context
  • Bladder boundary segmentation
  • Cystocele grading
  • Regression forest
  • Symphysis pubis detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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