To construct an automatic segmentation model to segment female urine control anatomy on MRI images based on deep learning methods in order to improve the segmentation efficiency and accuracy.

A dataset comprising 49 female pelvic floor muscle MRI images [30 women with varying degrees of pelvic organ prolapse (POP) and 19 healthy individuals], obtained from Faculty of Biomedical Engineering and Medical Imaging in Army Medical University, was used for model training and testing. The dataset was split into a training set (17 normal cases and 22 POP cases) and a testing set (4 normal cases and 6 POP cases) in a ratio of 8∶2. The training set was used to train UNet, UNet+++, Dense UNet, and UNet++ models separately, and then input into each network. The model achieving the highest testing accuracy was selected as the backbone network.

Under the training of UNet, UNet+++, Dense UNet, and UNet++, the 4 models achieved average Dice similarity coefficients of 61.82%, 57.94%, 57.63%, and 62.76%, respectively, for the segmentation of 5 anatomical structures (compressor urethrae, urethra sphincter body, bladder wall, bladder cavity and urethra submucosa). The corresponding Intersection over Union (IoU) score was 49.74%, 46.59%, 46.07%, and 49.44%, while the accuracy rate was 61.74%, 55.03%, 59.23%, and 61.91%, respectively for the 4 models. Notably, UNet++ consistently outperformed UNet, UNet+++, and Dense UNet across the 3 metrics, indicating that UNet++ achieved the highest overall segmentation accuracy.

In UNet, UNet++, Dense UNet and UNet++ for automatic segmentation of 5 female urine control anatomical elements, UNet++ achieves the best overall segmentation accuracy.