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Automatic segmentation of female urine control anatomical elements and related structures in MRI images based on deep learning
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Ziqin ZHANG1, Yi WU2, Xiaoqin ZHANG2, Zhou XU2, Ling LEI3, Yanzhou WANG4, Yan WANG1
Journal of Army Medical University | 2025, 47(14) : 1568 - 1576
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Journal of Army Medical University | 2025, 47(14): 1568-1576
• Monographic Reports •
Automatic segmentation of female urine control anatomical elements and related structures in MRI images based on deep learning
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Ziqin ZHANG1, Yi WU2, Xiaoqin ZHANG2, Zhou XU2, Ling LEI3, Yanzhou WANG4, Yan WANG1
Affiliations
  • 1School of Mathematical Sciences,Chongqing Normal University,Chongqing
  • 2Department of Digital Medicine,Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing
  • 3Department of Gynecology,Anshun People’s Hospital,Anshun,Guizhou
  • 4Department of Obstetrics and Gynecology,First Affiliated Hospital,Army Medical University (Third Military Medical University),Chongqing,China
Published: 2025-07-30 doi: 10.16016/j.2097-0927.202406055
Outline
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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.

deep learning  /  image segmentation  /  intelligent assisted diagnosis  /  nuclear magnetic resonance image
张子沁, 吴毅, 张小勤, 徐洲, 雷玲, 王延洲, 王艳. 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. 陆军军医大学学报, 2025 , 47 (14) : 1568 -1576 . DOI: 10.16016/j.2097-0927.202406055
Ziqin ZHANG, Yi WU, Xiaoqin ZHANG, Zhou XU, Ling LEI, Yanzhou WANG, Yan WANG. Automatic segmentation of female urine control anatomical elements and related structures in MRI images based on deep learning[J]. Journal of Army Medical University, 2025 , 47 (14) : 1568 -1576 . DOI: 10.16016/j.2097-0927.202406055
Year 2025 volume 47 Issue 14
16
4
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Article Info
doi: 10.16016/j.2097-0927.202406055
  • Receive Date:2024-06-17
  • Online Date:2026-10-28
  • Published:2025-07-30
Article Data
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History
  • Received:2024-06-17
  • Revised:2024-07-22
Affiliations
    1School of Mathematical Sciences,Chongqing Normal University,Chongqing
    2Department of Digital Medicine,Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing
    3Department of Gynecology,Anshun People’s Hospital,Anshun,Guizhou
    4Department of Obstetrics and Gynecology,First Affiliated Hospital,Army Medical University (Third Military Medical University),Chongqing,China
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表12种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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