Automatic cystocele severity grading in transperineal ultrasound by random forest regression

作者：

Highlights：

• First automatically computerized cystocele grading method on the transperineal ultrasound is developed in this study.

• The automatic cystocele grading on the transperineal ultrasound is realized with a two-phase random forest regression model.

• Auto-context features are helpful for our regression model to improve the cystocele grading results.

摘要

•First automatically computerized cystocele grading method on the transperineal ultrasound is developed in this study.•The automatic cystocele grading on the transperineal ultrasound is realized with a two-phase random forest regression model.•Auto-context features are helpful for our regression model to improve the cystocele grading results.

论文关键词：Cystocele grading,Symphysis pubis detection,Bladder boundary segmentation,Auto-context,Regression forest

论文评审过程：Received 25 January 2016, Revised 21 June 2016, Accepted 21 September 2016, Available online 22 September 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.033