Automatic segmentation of female urine control anatomical elements and related structures in MRI images based on deep learning
10.16016/j.2097-0927.202406055
- VernacularTitle:基于深度学习的MRI影像女性尿控解剖元件及相关结构的自动分割
- Author:
Ziqin ZHANG
1
;
Yi WU
;
Xiaoqin ZHANG
;
Zhou XU
;
Ling LEI
;
Yanzhou WANG
;
Yan WANG
Author Information
1. 重庆师范大学数学科学学院
- Keywords:
deep learning;
image segmentation;
intelligent assisted diagnosis;
nuclear magnetic resonance image
- From:
Journal of Army Medical University
2025;47(14):1568-1576
- CountryChina
- Language:Chinese
-
Abstract:
Objective 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.Methods 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.Results 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.Conclusion In UNet,UNet++,Dense UNet and UNet++for automatic segmentation of 5 female urine control anatomical elements,UNet++achieves the best overall segmentation accuracy.
