Automatic cystocele severity grading in transperineal ultrasound by random forest regression

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• 作者：Dong Ni^aAuthor Vitae ; Xing Ji^aAuthor Vitae ; Min Wu^bAuthor Vitae ; Wenlei Wang^bAuthor Vitae ; Xiaoshuang Deng^bAuthor Vitae ; Zhongyi Hu^cAuthor Vitae ; Tianfu Wang^aAuthor Vitae ; Dinggang Shen^d ; ^eAuthor Vitae ; Jie-Zhi Cheng^a ; ^{jiezhicheng@gmail.com}Author Vitae ; Huifang Wang^b ; ^{kuangwhf2006@126.com}Author Vitae
• 关键词：Cystocele grading ; Symphysis pubis detection ; Bladder boundary segmentation ; Auto-context ; Regression forest
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：551-560
• 全文大小：5788 K
• 卷排序：63

文摘

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.