title: Automatic Cystocele Severity Grading in Ultrasound by Spatio-Temporal Regression.
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.
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Ni
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Ji
authors.affiliation: Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Jie-Zhi
authors.initials: JZ
authors.lastname: Cheng
authors.affiliation: Department of Ultrasound, Shenzhen Second Peoples Hospital, Shenzhen, China.
authors.forename: Huifang
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Nursing, Centre for Smart Health, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Baiying
authors.initials: B
authors.lastname: Lei
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000328:Adult; D000465:Algorithms; D052858:Cystocele / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000628:therapy; D005260:Female; D006801:Humans; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014463:Ultrasonography / Q000379:methods*; D055815:Young Adult
keywords: Cystocele; Regression; Spatio-temporal; Ultrasound