Ni Dong, Ji Xing, Gao Yaozong, Cheng Jie-Zhi, Wang Huifang, Qin Jing, Lei Baiying, Wang Tianfu, Wu Guorong, Shen Dinggang
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC 27599, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:247-255. doi: 10.1007/978-3-319-46723-8_29. Epub 2016 Oct 2.
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.
膀胱膨出是女性的常见疾病。准确评估膀胱膨出的严重程度对于治疗方案的选择非常重要。经会阴超声（US）最近已成为膀胱膨出分级的一种替代工具。膀胱膨出的严重程度通常通过在瓦尔萨尔瓦动作期间手动测量膀胱相对于耻骨联合（SP）的最大下降距离（MDB）来评估。然而，这个过程既耗时又依赖操作人员。在本研究中，我们提出了一种从经会阴超声视频中自动进行膀胱膨出分级的方案。我们提出了一个两层的时空回归模型来识别耻骨联合的中轴线和下端，并分割膀胱，这是测量最大下降距离的关键任务。在时空域中提取外观和上下文特征以辅助解剖结构检测。对85个经会阴超声视频的实验结果表明，我们的方法显著优于当前最先进的回归方法。
