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
Keywords: 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.


. VOLLENHOVEN, B. J, LAWRENCE, A. S, HEALY, D. L. Uterine fibroids: A clinical review[J]. British journal of obstetrics & gynaecology, 2010, 97(4):285-298.

. Li S , Chen B , Sheng B , et al. The associations between serum vitamin D, calcium and uterine fibroids in Chinese women: a case-controlled study[J]. The Journal of international medical research, 2020, 48(5):030006052092349.

. Zhao W P, Chen J Y, Zhang L, et al. Feasibility of ultrasound-guided high intensity focused ultrasound ablating uterine fibroids with hyperintense on T2-weighted MR imaging[J]. European Journal of Radiology, 2013, 82(1).

. Yao J, Chen D, Lu W, et al. Uterine fibroid segmentation and volume measurement on MRI[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2006.

. Khotanlou H , Fallahi A , Oghabian M A , et al. Segmentation of uterine fibroid on MR images based on chan-vese level set method and shape prior model[J]. Biomedical Engineering Applications Basis & Communications, 2014, 26(02):1450030-.

. Fallahi A , Pooyan M , Ghanaati H , et al. Uterine Segmentation and Volume Measurement in Uterine Fibroid Patients' MRI Using Fuzzy C-Mean Algorithm and Morphological Operations[J]. Iranian Journal of Radiology, 2011, 08(03):150-156.

. Rundo L, Militello C, Vitabile S, et al. Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments[J]. Medical & Biological Engineering & Computing, 2016, 54(7): 1071-1084.

. Kurata Y, Nishio M , Kido A , et al. Automatic segmentation of the uterus on MRI using a convolutional neural network[J]. Computers in Biology and Medicine, 2019, 114:103438.

. He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]. computer vision and pattern recognition, 2016: 770-778.

. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-Net: Learning Where to Look for the Pancreas[J]. 2018.

. Li X, Sun X, Meng Y, et al. Dice Loss for Data-imbalanced NLP Tasks.[J]. arXiv: Computation and Language, 2019.

. Kingma,D. P., & Ba, J. " Adam: a method for stochastic optimization. Computer Science, "2014.

. Leal,Y.,Gonzalez-Abril, L.,Ruiz, M.,Lorencio, C.,Bondia, J.,Vehi, J. "Un nuevo enfoque para detectarmediciones de glucosaerroneasen los sistemas de monitorizacioncont inuos de glucose,", JARCA 2012, vol. 15, pp. 17, 2012.

. Fernandez,P. C.,Taladriz,C. C.,Alcaide,F. G.,Sacchi,L., Bellazzi,R., Aguilera, E, J, G. "Extraccion de reglas temporales complejas para la deteccion de fallos del tratamiento antiretroviral,"2008.