MRI Image Segmentation System of Uterine Fibroids Based on AR-Unet Network


  • TANG Chun-ming School of Artificial Intelligence Institute, Tiangong University, Tianjin, 300387,CHINA
  • LIU Dong School of Life Sciences, Tiangong University, Tianjin, 300387,CHINA
  • YU Xiang Country Center for Engineering Practice and Training, Tiangong University, Tianjin, 300387, CHINA


Uterine fibroids MRI image segmentation, Attention ResNet101-Unet, Attention mechanism


Uterine fibroids are the most common benign tumors in female reproductive organs. The segmentation of uterine fibroids is crucial for accurate treatment. This paper proposes a new uterine fibroids MRI T2W image segmentation network AR-Unet (Attention Resnet101-Unet), which uses the deep neural network ResNet101 as the front end of feature extraction, extracts image semantic information, and combines U-net design ideas to build a network structure. The attention gate module is added before the upsampling and downsampling feature maps are spliced. We tested a total of 123 uterine fibroids MRI T2W images from 13 patients. The segmentation results were verified with expert-defined manual segmentation results. The average Dice coefficient, IOU value, sensitivity and specificity of all segmented images were 0.9044, 0.8443, 88.55% and 94.56%, the performance is better than ResNet101-Unet and Attention-Unet models, and finally the network is encapsulated into an auxiliary diagnostic system.


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How to Cite

Chun-ming , T. ., Dong , L. ., & Xiang , Y. . (2020). MRI Image Segmentation System of Uterine Fibroids Based on AR-Unet Network. American Scientific Research Journal for Engineering, Technology, and Sciences, 71(1), 1–10. Retrieved from