Endometrial cancer lesion region segmentation based on large kernel convolution and combined attention_Journal of Biomedical Engineering

Authors：

PENG Rushu , ZENG Qinghao , HE Bin , LIU Junjie ,  XIAO Zhang

School of Mechanical Engineering, University of South China, Hengyang, Hunan 421200, P. R. China;

Corresponding author：

XIAO Zhang, Email: 2013000885@usc.edu.cn

Keywords：

Deep learning; Endometrial cancer; Computed tomography; Image segmentation

DOI：

10.7507/1001-5515.202502023

Video：

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Abstract Full text Figures/Tables Video References Cited by

Endometrial cancer (EC) is one of the most common gynecological malignancies, with an increasing incidence rate worldwide. Accurate segmentation of lesion areas in computed tomography (CT) images is a critical step in assisting clinical diagnosis. In this study, we propose a novel deep learning-based segmentation model, termed spatial choice and weight union network (SCWU-Net), which incorporates two newly designed modules: the spatial selection module (SSM) and the combination weight module (CWM). The SSM enhances the model’s ability to capture contextual information through deep convolutional blocks, while the CWM, based on joint attention mechanisms, is employed within the skip connections to further boost segmentation performance. By integrating the strengths of both modules into a U-shaped multi-scale architecture, the model achieves precise segmentation of EC lesion regions. Experimental results on a public dataset demonstrate that SCWU-Net achieves a Dice similarity coefficient (DSC) of 82.98%, an intersection over union (IoU) of 78.63%, a precision of 92.36%, and a recall of 84.10%, its overall performance is significantly outperforming other state-of-the-art models. This study enhances the accuracy of lesion segmentation in EC CT images and holds potential clinical value for the auxiliary diagnosis of endometrial cancer.

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6.	李芳, 张果, 王建六. 子宫内膜癌筛查研究进展. 中国妇产科临床杂志, 2025, 26(2): 152-155.
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14.	金涛, 王震, 李昭蒂. 基于多尺度注意力U-Net的医学肝脏计算机断层扫描图片分割算法. 哈尔滨工程大学学报, 2025, 46(3): 529-539.
15.	扈拯宁, 黄强, 谢尧, 等. 基于卷积神经网络的卵巢囊腺瘤CT图像病灶分割. 计算机应用与软件, 2025, 42(1): 189-196.
16.	石军, 王天同, 朱子琦, 等. 基于深度学习的医学图像分割方法综述. 中国图象图形学报, 2025, 30(6): 2161-2186.
17.	Rai H M. Cancer detection and segmentation using machine learning and deep learning techniques: a review. Multimedia Tools and Applications, 2024, 83(9): 27001-27035.
18.	张玮智, 于谦, 苏金善, 等. 从U-Net到Transformer: 深度模型在医学图像分割中的应用综述. 计算机应用, 2024, 44(1): 204-222.
19.	Wang Y, Chen Z, Liu C, et al. Radiomics-based fertility-sparing treatment in endometrial carcinoma: a review. Insights into Imaging, 2023, 14(1): 127.
20.	Reeder K, Lee H. Impact of artificial intelligence on US medical students' choice of radiology. Clinical Imaging, 2022, 81: 67-71.
21.	Chen X, Wang Y, Shen M, et al. Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. European Radiology, 2020, 30(9): 4985-4994.
22.	Kurata Y, Nishio M, Moribata Y, et al. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Scientific Reports, 2021, 11(1): 14440.
23.	Mao W, Chen C, Gao H, et al. A deep learning-based automatic staging method for early endometrial cancer on MRI images. Frontiers in Physiology, 2022, 13: 974245.
24.	Li D, Hu R, Li H, et al. Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography. Abdominal Radiology, 2021, 46(11): 5316-5324.
25.	Kim N, Chang J S, Kim Y B, et al. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers. Radiation Oncology, 2020, 15(1): 106.
26.	Li C, Qiang Y, Sultan R I, et al. FocalUNETR: a focal transformer for boundary-aware prostate segmentation using CT images//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 592-602.
27.	Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: a survey. Computational Visual Media, 2022, 8(3): 331-368.
28.	Yang L, Zhang R Y, Li L, et al. Simam: a simple, parameter-free attention module for convolutional neural networks//International Conference on Machine Learning, Vienna: PMLR, 2021: 11863-11874.
29.	Yao M, Zhao G, Zhang H, et al. Attention spiking neural networks. IEEE Trans Pattern Anal Mach Intell, 2023, 45(8): 9393-9410.
30.	Tang D, Li C, Du T, et al. ECPC-IDS: a benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions. Computers in Biology and Medicine, 2024, 171: 108217.
31.	潘丹, 骆根强, 曾安. 基于Transformer和卷积神经网络双并行分支编码器神经网络的冠状动脉分割. 生物医学工程学杂志, 2024, 41(6): 1195-1203,1212.
32.	武星, 陶晨杰, 李智, 等. 基于通道权重和数据效用特征的医学图像分割数据增强方法. 生物医学工程学杂志, 2024, 41(2): 220-227,236.
33.	Ranneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich: Springer International Publishing, 2015: 234-241.
34.	Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(12): 2481-2495.
35.	Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the European conference on computer vision (ECCV), Munich: EACV, 2018: 801-818.
36.	Wang S, Li L, Zhuang X. AttU-Net: attention U-Net for brain tumor segmentation//International MICCAI brainlesion workshop. Cham: Springer International Publishing, 2021: 302-311.
37.	Xiao Z, Du M, Liu J, et al. EA-UNet based segmentation method for OCT image of uterine cavity. Photonics, 2023, 10(1): 73.
38.	Jin S, Yu S, Peng J, et al. A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning. Scientific Reports, 2023, 13(1): 6762.
39.	Chen B, Liu Y, Zhang Z, et al. Transattunet: multi-level attention-guided U-net with transformer for medical image segmentation. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 8(1): 55-68.
40.	Tang H, Chen Y, Wang T, et al. HTC-Net: a hybrid cnn-transformer framework for medical image segmentation. Biomedical Signal Processing and Control, 2024, 88: 105605.

1. Hong I S. Endometrial stem/progenitor cells: properties, origins, and functions. Genes Dis, 2022, 10(3): 931-947.
2. 曹瑾瑾, 李佳, 李奇灵, 等. 子宫内膜癌筛查策略. 中国实用妇科与产科杂志, 2023, 39(11): 1062-1065.
3. Ge L, Liu G, Hu K, et al. A new risk index combining d-dimer, fibrinogen, HE4, and CA199 differentiates suspecting endometrial cancer from patients with abnormal vaginal bleeding or discharge. Technol Cancer Res Treat, 2020, 19: 1533033819901117.
4. Huang G Q, Xi Y Y, Zhang C J, et al. Serum human epididymis protein 4 combined with carbohydrate antigen 125 for endometrial carcinoma diagnosis: a meta-analysis and systematic review. Genet Test Mol Biomarkers, 2019, 23(8): 580-588.
5. Wang J, Wang Y, Li Y, et al. Trend and characteristics of endometrial cancer in guangzhou from 2000 to 2020. Eur J Obstet Gynecol Reprod Biol, 2025, 305: 381-386.
6. 李芳, 张果, 王建六. 子宫内膜癌筛查研究进展. 中国妇产科临床杂志, 2025, 26(2): 152-155.
7. Baker-Rand H, Kitson S J. Recent advances in endometrial cancer prevention, early diagnosis and treatment. Cancers, 2024, 16(5): 1028.
8. 王尧鑫, 吴昆华. 子宫内膜癌的影像组学研究进展. 中国医学影像学杂志, 2022, 30(6): 630-634.
9. Shen Y, Yang W, Liu J, et al. Minimally invasive approaches for the early detection of endometrial cancer. Molecular Cancer, 2023, 22(1): 53.
10. 王晓君, 刘军秀. 人工智能在妇科恶性肿瘤中的进展. 中山大学学报, 2025, 46(1): 21-29.
11. 冯龙锋, 陈英, 周滔辉, 等. CT图像肺及肺病变区域分割方法综述. 中国图象图形学报, 2022, 27(3): 722-749.
12. 田苗苗, 支力佳, 张少敏, 等. 医学CT影像超分辨率深度学习方法综述. 计算机工程与应用, 2024, 60(3): 44-60.
13. Guo L, Yu Y, Yang F, et al. Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis. Chinese Medical Journal, 2023, 136(9): 1047-1056.
14. 金涛, 王震, 李昭蒂. 基于多尺度注意力U-Net的医学肝脏计算机断层扫描图片分割算法. 哈尔滨工程大学学报, 2025, 46(3): 529-539.
15. 扈拯宁, 黄强, 谢尧, 等. 基于卷积神经网络的卵巢囊腺瘤CT图像病灶分割. 计算机应用与软件, 2025, 42(1): 189-196.
16. 石军, 王天同, 朱子琦, 等. 基于深度学习的医学图像分割方法综述. 中国图象图形学报, 2025, 30(6): 2161-2186.
17. Rai H M. Cancer detection and segmentation using machine learning and deep learning techniques: a review. Multimedia Tools and Applications, 2024, 83(9): 27001-27035.
18. 张玮智, 于谦, 苏金善, 等. 从U-Net到Transformer: 深度模型在医学图像分割中的应用综述. 计算机应用, 2024, 44(1): 204-222.
19. Wang Y, Chen Z, Liu C, et al. Radiomics-based fertility-sparing treatment in endometrial carcinoma: a review. Insights into Imaging, 2023, 14(1): 127.
20. Reeder K, Lee H. Impact of artificial intelligence on US medical students' choice of radiology. Clinical Imaging, 2022, 81: 67-71.
21. Chen X, Wang Y, Shen M, et al. Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. European Radiology, 2020, 30(9): 4985-4994.
22. Kurata Y, Nishio M, Moribata Y, et al. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Scientific Reports, 2021, 11(1): 14440.
23. Mao W, Chen C, Gao H, et al. A deep learning-based automatic staging method for early endometrial cancer on MRI images. Frontiers in Physiology, 2022, 13: 974245.
24. Li D, Hu R, Li H, et al. Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography. Abdominal Radiology, 2021, 46(11): 5316-5324.
25. Kim N, Chang J S, Kim Y B, et al. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers. Radiation Oncology, 2020, 15(1): 106.
26. Li C, Qiang Y, Sultan R I, et al. FocalUNETR: a focal transformer for boundary-aware prostate segmentation using CT images//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 592-602.
27. Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: a survey. Computational Visual Media, 2022, 8(3): 331-368.
28. Yang L, Zhang R Y, Li L, et al. Simam: a simple, parameter-free attention module for convolutional neural networks//International Conference on Machine Learning, Vienna: PMLR, 2021: 11863-11874.
29. Yao M, Zhao G, Zhang H, et al. Attention spiking neural networks. IEEE Trans Pattern Anal Mach Intell, 2023, 45(8): 9393-9410.
30. Tang D, Li C, Du T, et al. ECPC-IDS: a benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions. Computers in Biology and Medicine, 2024, 171: 108217.
31. 潘丹, 骆根强, 曾安. 基于Transformer和卷积神经网络双并行分支编码器神经网络的冠状动脉分割. 生物医学工程学杂志, 2024, 41(6): 1195-1203,1212.
32. 武星, 陶晨杰, 李智, 等. 基于通道权重和数据效用特征的医学图像分割数据增强方法. 生物医学工程学杂志, 2024, 41(2): 220-227,236.
33. Ranneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich: Springer International Publishing, 2015: 234-241.
34. Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(12): 2481-2495.
35. Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the European conference on computer vision (ECCV), Munich: EACV, 2018: 801-818.
36. Wang S, Li L, Zhuang X. AttU-Net: attention U-Net for brain tumor segmentation//International MICCAI brainlesion workshop. Cham: Springer International Publishing, 2021: 302-311.
37. Xiao Z, Du M, Liu J, et al. EA-UNet based segmentation method for OCT image of uterine cavity. Photonics, 2023, 10(1): 73.
38. Jin S, Yu S, Peng J, et al. A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning. Scientific Reports, 2023, 13(1): 6762.
39. Chen B, Liu Y, Zhang Z, et al. Transattunet: multi-level attention-guided U-net with transformer for medical image segmentation. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 8(1): 55-68.
40. Tang H, Chen Y, Wang T, et al. HTC-Net: a hybrid cnn-transformer framework for medical image segmentation. Biomedical Signal Processing and Control, 2024, 88: 105605.

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