Medical nucleus image segmentation network based on convolution and attention mechanism_Journal of Biomedical Engineering

Authors：

ZHI Peipei ¹ ,  DENG Jianzhi ^1,2 , ZHONG Zhenxiao ¹

1. School of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China;
2. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China;

Corresponding author：

DENG Jianzhi, Email: dengjianzhi@163.com

Keywords：

Nucleus segmentation; Feature extraction; Information fusion; Residual network; Coordinate attention

DOI：

10.7507/1001-5515.202112013

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.

Citation： ZHI Peipei, DENG Jianzhi, ZHONG Zhenxiao. Medical nucleus image segmentation network based on convolution and attention mechanism. Journal of Biomedical Engineering, 2022, 39(4): 730-739. doi: 10.7507/1001-5515.202112013 Copy

1.	韩继能, 谢嘉伟, 顾松, 等. 基于全景病理图像细胞密度和异型特征的胶质瘤自动分级. 生物医学工程学杂志, 2021, 38(6): 1062-1071.
2.	Cayir S, Serteli E A, Ayalti S, et al. Segmentation of the main structures in hematoxylin and eosin images//2018 Signal Processing and Communications Applications Conference (SIU). Piscataway: IEEE, 2018: 1-4.
3.	Guo G K, Liu W, Yu L L, et al. Particle image segmentation based on improved fast and watershed algorithm. Powder Technology in China, 2019, 25(2): 61-67.
4.	Wang H Y, Zhao H. Canny operator edge detection algorithm based on mathematical morphology preprocessing. Journal of Heze University, 2019, 41(2): 25-29.
5.	Eka P M, Annggraeny F T, Muttaqin F, et al. Global and adaptive thresholding technique for white blood cell image segmentation. J Phys Conf Ser, 2020, 1569(2): 022054-022060.
6.	Gu L, Zheng Y, Bise R, et al. Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels)//2017 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Berlin, Germany: Springer, 2017: 702-710.
7.	Nguyen N Q, Lee S W. Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access, 2019, 7(1): 33795-33808.
8.	Zheng T Y, Tang C, Lei Z K. Multi scale retinal vessel segmentation based on full convolution neural network. Acta Optica Sinica, 2019, 39(2): 119-126.
9.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MIC-CAI). Heidelberg: Springer, 2015: 234-241.
10.	Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep resdiual UNet. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753.
11.	汪豪, 吉邦宁, 何刚, 等. 一种提高直肠癌诊断精度的基于U型网络和残差块的电子计算机断层扫描图像分割算法. 生物医学工程学杂志, 2022, 39(1): 166-174, 184.
12.	Yao H B, Bian J W, Cong J W, et al. Medical image segmentation model based on local sparse shape representation. Progress in Laser and Optoelectronics, 2018, 55(5): 169-174.
13.	Zhang Lifeng, Li Bin. InvUnet: inverse the Unet for nuclear segmentation in H&E stained images//2020 12th International Conference on Advanced Computational Intelligence (ICACI), Dali: IEEE, 2020: 251-256.
14.	Graham S, Chen H, Gamper J, et al. MILD-Net: minimal information loss dilated network for gland instance segmentation in colon histology images. Medical Image Analysis, 2019, 52: 199-211.
15.	He H, Zhang C, Chen J, et al. A hybrid-attention nested UNet for nuclear segmentation in histopathological images. Frontiers in Molecular Biosciences, 2021, 8: 614174.
16.	宋杰, 肖亮, 练智超, 等. 基于深度学习的数字病理图像分割综述与展望. 软件学报, 2021, 32(5): 1427-1460.
17.	Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv, 2017: 1704.04861.
18.	袁鑫, 郑秀娟, 吉彬, 等. 基于多尺度残差卷积神经网络的视杯视盘联合分割. 生物医学工程学杂志, 2020, 37(5): 875-884.
19.	Jie H, Li S, Gang S, et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017: 7132-7141.
20.	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//2018 European Conference on Computer Vision (ECCV). Munich: Springer, 2018, 10(8): 168-203.
21.	Hou Qibin, Zhou Daquan, Feng Jiashi. Coordinate attention for efficient mobile network design//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ: IEEE, 2021: 13708-13717.
22.	尤轩昂, 赵鹏, 慕晓冬, 等. 融合注意力机制与密集多尺度特征的异质噪声虹膜分割方法. 激光与光电子学进展, 2022, 59(4): 101-112.
23.	Btehaz N, Rahman M S. MultiResUNet: rethinking the UNet architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121(1): 74-87.
24.	Caicedo J C, Goodman A, Karhohs K W, et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat Methods, 2019, 16(12): 1247-1253.
25.	Naylor P, Lae M, Reyal F, et al. Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging, 2019, 38(2): 448-459.
26.	He Y S, Jiang J L, Yu H, et al. Comarison of dice similarity coefficient and Hausdorff distance in image segmentation. Chinese Journal of Medical Physics, 2013, 36(11): 1307-1311.
27.	Hao X Y, Xiong J F, Xu D, et al. Lung tumor segmentation based on 3D U-net with dual attention mechanism. Chinese Journal of Image Graphics, 2020, 25(10): 2119-2127.
28.	Peng Hongxing, Xue C, Shao Yuanyuan, et al. Semantic segmentation of litchi branches using DeepLabv3+ model. IEEE Access, 2020, 8: 164546-164555.

1. 韩继能, 谢嘉伟, 顾松, 等. 基于全景病理图像细胞密度和异型特征的胶质瘤自动分级. 生物医学工程学杂志, 2021, 38(6): 1062-1071.
2. Cayir S, Serteli E A, Ayalti S, et al. Segmentation of the main structures in hematoxylin and eosin images//2018 Signal Processing and Communications Applications Conference (SIU). Piscataway: IEEE, 2018: 1-4.
3. Guo G K, Liu W, Yu L L, et al. Particle image segmentation based on improved fast and watershed algorithm. Powder Technology in China, 2019, 25(2): 61-67.
4. Wang H Y, Zhao H. Canny operator edge detection algorithm based on mathematical morphology preprocessing. Journal of Heze University, 2019, 41(2): 25-29.
5. Eka P M, Annggraeny F T, Muttaqin F, et al. Global and adaptive thresholding technique for white blood cell image segmentation. J Phys Conf Ser, 2020, 1569(2): 022054-022060.
6. Gu L, Zheng Y, Bise R, et al. Semi-supervised learning for biomedical image segmentation via forest oriented super pixels(voxels)//2017 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Berlin, Germany: Springer, 2017: 702-710.
7. Nguyen N Q, Lee S W. Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access, 2019, 7(1): 33795-33808.
8. Zheng T Y, Tang C, Lei Z K. Multi scale retinal vessel segmentation based on full convolution neural network. Acta Optica Sinica, 2019, 39(2): 119-126.
9. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MIC-CAI). Heidelberg: Springer, 2015: 234-241.
10. Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep resdiual UNet. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753.
11. 汪豪, 吉邦宁, 何刚, 等. 一种提高直肠癌诊断精度的基于U型网络和残差块的电子计算机断层扫描图像分割算法. 生物医学工程学杂志, 2022, 39(1): 166-174, 184.
12. Yao H B, Bian J W, Cong J W, et al. Medical image segmentation model based on local sparse shape representation. Progress in Laser and Optoelectronics, 2018, 55(5): 169-174.
13. Zhang Lifeng, Li Bin. InvUnet: inverse the Unet for nuclear segmentation in H&E stained images//2020 12th International Conference on Advanced Computational Intelligence (ICACI), Dali: IEEE, 2020: 251-256.
14. Graham S, Chen H, Gamper J, et al. MILD-Net: minimal information loss dilated network for gland instance segmentation in colon histology images. Medical Image Analysis, 2019, 52: 199-211.
15. He H, Zhang C, Chen J, et al. A hybrid-attention nested UNet for nuclear segmentation in histopathological images. Frontiers in Molecular Biosciences, 2021, 8: 614174.
16. 宋杰, 肖亮, 练智超, 等. 基于深度学习的数字病理图像分割综述与展望. 软件学报, 2021, 32(5): 1427-1460.
17. Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv, 2017: 1704.04861.
18. 袁鑫, 郑秀娟, 吉彬, 等. 基于多尺度残差卷积神经网络的视杯视盘联合分割. 生物医学工程学杂志, 2020, 37(5): 875-884.
19. Jie H, Li S, Gang S, et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017: 7132-7141.
20. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module//2018 European Conference on Computer Vision (ECCV). Munich: Springer, 2018, 10(8): 168-203.
21. Hou Qibin, Zhou Daquan, Feng Jiashi. Coordinate attention for efficient mobile network design//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ: IEEE, 2021: 13708-13717.
22. 尤轩昂, 赵鹏, 慕晓冬, 等. 融合注意力机制与密集多尺度特征的异质噪声虹膜分割方法. 激光与光电子学进展, 2022, 59(4): 101-112.
23. Btehaz N, Rahman M S. MultiResUNet: rethinking the UNet architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121(1): 74-87.
24. Caicedo J C, Goodman A, Karhohs K W, et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat Methods, 2019, 16(12): 1247-1253.
25. Naylor P, Lae M, Reyal F, et al. Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging, 2019, 38(2): 448-459.
26. He Y S, Jiang J L, Yu H, et al. Comarison of dice similarity coefficient and Hausdorff distance in image segmentation. Chinese Journal of Medical Physics, 2013, 36(11): 1307-1311.
27. Hao X Y, Xiong J F, Xu D, et al. Lung tumor segmentation based on 3D U-net with dual attention mechanism. Chinese Journal of Image Graphics, 2020, 25(10): 2119-2127.
28. Peng Hongxing, Xue C, Shao Yuanyuan, et al. Semantic segmentation of litchi branches using DeepLabv3+ model. IEEE Access, 2020, 8: 164546-164555.

Previous Article
Lung parenchyma segmentation based on double scale parallel attention network
Next Article
Comparison of wall filter algorithms for ultrasonic microvascular imaging

Journal of Biomedical Engineering

Medical nucleus image segmentation network based on convolution and attention mechanism

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content