White blood segmentation based on dual path and atrous spatial pyramid pooling_Journal of Biomedical Engineering

Authors：

LI Zuoyong ^1,2 , LU Yan ² ,  CAO Xinrong ^1,2 , QIU Lida ³ , QIN Xuejun ⁴

1. College of Computer and Control Engineering, Minjiang University, Fuzhou 350121, P. R. China;
2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou 350121, P. R. China;
3. College of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350121, P. R. China;
4. Department of Clinical Laboratory, The People’s Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou 350001, P. R. China;

Corresponding author：

CAO Xinrong, Email: cxrxmu@163.com

Keywords：

Image segmentation; White blood cell segmentation; Convolutional neural network; Dual path network; Atrous spatial pyramid pooling

DOI：

10.7507/1001-5515.202107043

Video：

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Abstract Full text Figures/Tables Video References Cited by

The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.

Citation： LI Zuoyong, LU Yan, CAO Xinrong, QIU Lida, QIN Xuejun. White blood segmentation based on dual path and atrous spatial pyramid pooling. Journal of Biomedical Engineering, 2022, 39(3): 471-479. doi: 10.7507/1001-5515.202107043 Copy

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2.	Wang C, Zhang H, Li Z, et al. White blood cell image segmentation based on color component combination and contour fitting. Curr Bioinform, 2020, 15(5): 463-471.
3.	Hafeez H, Yan P, Guoliang L. Image processing approach for segmentation of WBC nuclei based on k-means clustering// 4th International Conference on Image and Graphics Processing. Sanya: ACM, 2021: 175-181.
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7.	Zheng X, Wang Y, Wang G. White blood cell segmentation using expectation-maximization and automatic support vector machine learning. Data Acquis Process, 2013, 28: 614-619.
8.	Zheng X, Wang Y, Wang G, et al. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 2018, 107: 55-71.
9.	Khan S, Javed M H, Ahmed E, et al. Facial recognition using convolutional neural networks and implementation on smart glasses// 2019 International Conference on Information Science and Communication Technology. Jeju Island: IEEE, 2019: 1-6.
10.	He R, Wu X, Sun Z, et al. Wasserstein CNN: learning invariant features for NIR-VIS face recognition. IEEE Trans Pattern Anal Mach Intell, 2018, 41(7): 1761-1773.
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13.	Tian C, Xu Y, Li Z, et al. Attention-guided CNN for image denoising. Neural Netw, 2020, 124: 117-129.
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16.	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. Cham: Springer, 2015: 234-241.
17.	He K, Gkioxari G, Dollár P, et al. Mask R-CNN// 2017 International Conference on Computer Vision. Venice: IEEE, 2017: 2961-2969.
18.	Fan H, Zhang F, Xi L, et al. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophoton, 2019, 12(7): e201800488.
19.	Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks// 2015 Advances in Neural Information Processing Systems. Montreal: NIPS, 2015: 91-99.
20.	Chen Y, Li J, Xiao H, et al. Dual path networks// 2017 Proceedings of the Advances in Neural Information Processing Systems. Long Beach: NIPS, 2017: 4467-4475.
21.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// 2016 Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
22.	Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks// 2017 Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708.
23.	Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv, 2017: 1706.05587.
24.	Iglovikov V, Shvets A. TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. arXiv preprint arXiv, 2018: 1801.05746.
25.	Lu Y, Qin X, Fan H, et al. WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Appl Soft Comput, 2021, 101: 107006.
26.	Fan H, Zhang F, Wang R, et al. Correlation-aware deep generative model for unsupervised anomaly detection// Lauw H, Wong R W, Ntoulas A, et al. Advances in knowledge discovery and data mining. PAKDD 2020. Cham: Springer, 2020, 12085: 688-700.
27.	Mondal P, Prodhan U, Al Mamun M, et al. Segmentation of white blood cells using fuzzy c means segmentation algorithm. IOSR-JCE, 2014, 1(16): 1-5.
28.	Singh V K, Romani S, Rashwan H A, et al. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification// 2018 Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2018: 833-840.
29.	Zhou C, Fan H, Li Z. Tonguenet: Accurate localization and segmentation for tongue images using deep neural networks. IEEE Access, 2019, 7: 148779-148789.
30.	Zhou X, Li Z, Xie H, et al. Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr Bioinform, 2020, 15(3): 187-195.

1. Zhou X, Wang C, Li Z, et al. Adaptive histogram thresholding-based leukocyte image segmentation// 2020 Advances in Intelligent Information Hiding and Multimedia Signal Processing. Singapore: Springer, 2020: 451-459.
2. Wang C, Zhang H, Li Z, et al. White blood cell image segmentation based on color component combination and contour fitting. Curr Bioinform, 2020, 15(5): 463-471.
3. Hafeez H, Yan P, Guoliang L. Image processing approach for segmentation of WBC nuclei based on k-means clustering// 4th International Conference on Image and Graphics Processing. Sanya: ACM, 2021: 175-181.
4. Puttamadegowda J, Prasannakumar S C. White blood cell segmentation using fuzzy c means and snake// 2016 International Conference on Computation System and Information Technology for Sustainable Solutions. Bengaluru: IEEE, 2016: 47-52.
5. Zhong Z, Wang T, Zeng K, et al. White blood cell segmentation via sparsity and geometry constraints. IEEE Access, 2019, 7: 167593-167604.
6. Di Ruberto C, Loddo A, Putzu L. A multiple classifier learning by sampling system for white blood cells segmentation// 2015 International Conference on Computer Analysis of Images and Patterns. Cham: Springer, 2015: 415-425.
7. Zheng X, Wang Y, Wang G. White blood cell segmentation using expectation-maximization and automatic support vector machine learning. Data Acquis Process, 2013, 28: 614-619.
8. Zheng X, Wang Y, Wang G, et al. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 2018, 107: 55-71.
9. Khan S, Javed M H, Ahmed E, et al. Facial recognition using convolutional neural networks and implementation on smart glasses// 2019 International Conference on Information Science and Communication Technology. Jeju Island: IEEE, 2019: 1-6.
10. He R, Wu X, Sun Z, et al. Wasserstein CNN: learning invariant features for NIR-VIS face recognition. IEEE Trans Pattern Anal Mach Intell, 2018, 41(7): 1761-1773.
11. Zhao Z Q, Zheng P, Xu S, et al. Object detection with deep learning: a review. IEEE Trans Neural Netw Learning Syst, 2019, 30(11): 3212-3232.
12. Sun P, Zhang R, Jiang Y, et al. Sparse R-CNN: end-to-end object detection with learnable proposals// 2021 Conference on Computer Vision and Pattern Recognition. Shenzhen: IEEE, 2021: 14454-14463.
13. Tian C, Xu Y, Li Z, et al. Attention-guided CNN for image denoising. Neural Netw, 2020, 124: 117-129.
14. Quan Y, Chen Y, Shao Y, et al. Image denoising using complex-valued deep CNN. Pattern Recogn, 2021, 111: 107639.
15. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation// 2015 Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440.
16. 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. Cham: Springer, 2015: 234-241.
17. He K, Gkioxari G, Dollár P, et al. Mask R-CNN// 2017 International Conference on Computer Vision. Venice: IEEE, 2017: 2961-2969.
18. Fan H, Zhang F, Xi L, et al. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophoton, 2019, 12(7): e201800488.
19. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks// 2015 Advances in Neural Information Processing Systems. Montreal: NIPS, 2015: 91-99.
20. Chen Y, Li J, Xiao H, et al. Dual path networks// 2017 Proceedings of the Advances in Neural Information Processing Systems. Long Beach: NIPS, 2017: 4467-4475.
21. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// 2016 Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
22. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks// 2017 Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708.
23. Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv, 2017: 1706.05587.
24. Iglovikov V, Shvets A. TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. arXiv preprint arXiv, 2018: 1801.05746.
25. Lu Y, Qin X, Fan H, et al. WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Appl Soft Comput, 2021, 101: 107006.
26. Fan H, Zhang F, Wang R, et al. Correlation-aware deep generative model for unsupervised anomaly detection// Lauw H, Wong R W, Ntoulas A, et al. Advances in knowledge discovery and data mining. PAKDD 2020. Cham: Springer, 2020, 12085: 688-700.
27. Mondal P, Prodhan U, Al Mamun M, et al. Segmentation of white blood cells using fuzzy c means segmentation algorithm. IOSR-JCE, 2014, 1(16): 1-5.
28. Singh V K, Romani S, Rashwan H A, et al. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification// 2018 Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2018: 833-840.
29. Zhou C, Fan H, Li Z. Tonguenet: Accurate localization and segmentation for tongue images using deep neural networks. IEEE Access, 2019, 7: 148779-148789.
30. Zhou X, Li Z, Xie H, et al. Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr Bioinform, 2020, 15(3): 187-195.

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