摘要
提出一种智能识别肛提肌裂孔的方法,利用端到端的编码器-解码器结构全卷积神经网络,结合自动上下文模型思想,分割出人体盆底超声图像中肛提肌裂孔,采用全连接条件随机场加强边缘约束,对分割结果实现精细化处理,实现肛提肌裂孔的智能识别.通过对372张盆底超声图像进行智能识别,并与医生手动标注结果对比,两者重合率达到95.16%,优于传统卷积神经网络模型,证实基于上下文及条件随机场的神经网络方法能有效识别肛提肌裂孔,具有重要临床应用价值.
We propose a novel deep learning based method to recognize the levator hiatus. The levator hiatus is segmented from the pelvic floor ultrasonic image by using the encoder-decoder architecture of full convolutional neural networks and combining with the auto-context model in an end-to-end manner. The full connection of conditional random fields is used to strengthen the edge constraint in order to refine the segmentation results. The experimental results on 372 pelvic floor ultrasonic images show that the dice ratio of our automatic recognition,compared with the labels of doctors,reaches about 95. 16%. The results demonstrate that the proposed method provides more accurate segmentation than the state-of-the-art methods and have a potential clinical value.
作者
胡鹏辉
王娜
王毅
王慧芳
汪天富
倪东
HU Penghui;WANG Na;WANG Yi;WANG Huifang;WANG Tianfu;and NI Dong(School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen 518060, Guangdong Province, P. R. China;Department of Ultrasound, Shenzhen No. 2 People＇s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, Guangdong Province, P. R. China)
出处
《深圳大学学报（理工版）》
EI
CAS
CSCD
北大核心
2018年第3期316-323,共8页
Journal of Shenzhen University(Science and Engineering)
基金
国家自然科学基金资助项目(61571304,61701312,81571758,81771922)
