Automatic segmentation of levator ani muscle in MRI images based on DenseUnet model

• Main Authors: XIANG Yongjia, WU Yi, ZHANG Xiaoqin, HU Xin, LIU Jingjing, LEI Ling, WANG Yanzhou, WANG Yan
• Format: Article
• Language: zho
• Published: Editorial Office of Journal of Third Military Medical University 2021-09-01
• Series: Di-san junyi daxue xuebao
• Subjects: convolutional neural network; image segmentation; intelligent assisted diagnosis
• Online Access: http://aammt.tmmu.edu.cn/Upload/rhtml/202102089.htm

Objective To construct a deep learning automatic segmentation model based on the magnetic resonance image (MRI) of the pelvic floor, and make the intelligent segmentation of the pelvic floor MR image so as to reduce the work intensity of doctors and improve the segmentation efficiency and accuracy of levator ani muscle. Methods Based on DenseUnet model, a network structure composed of encoder module, context extraction module and decoder module was established; In the context extraction module, we used dilated convolution and pyramid pooling module to overcome the disadvantages that Unet uses less context information and global information under different receptive fields. We employed the MRI data of 19 patients, including 14 normal cases, 1 case of grade 1 pelvic organ prolapse (POP1) and 2 cases of grade 2 pelvic organ prolapse (POP2) as training sets. One normal pelvic floor MRI image and 1 POP2 pelvic floor MR image were used for verification. Results The model can segment levator ani muscle in pelvic floor MR image automatically and effectively. Through verification, the average similarity coefficient of levator ani muscle in the test set is 77.1%, the average Hausdorff distance is 16 mm, and the average symmetry plane distance is 0.9 mm. The average similarity coefficient of levator ani muscle in normal volunteers was 81.2%, and that of POP2 female pelvic floor levator ani muscle was 74.5%. Conclusion The segmentation accuracy of DenseUnet model is better than that of Unet, ResUnet and Unet++. It has a strong practical value in the automatic segmentation task of levator ani muscle in MRI images. Through the automatic segmentation of levator ani muscle, the repetitive work of doctors is reduced, and the work efficiency is improved. At the same time, it also provides an alternative for the intelligent auxiliary diagnosis and treatment of pelvic organ prolapse.