Banded chromosome images recognition based on dense convolutional network with segmental recalibration_Journal of Biomedical Engineering

Authors：

LI Jianming ^1,2,3 ,  CHEN Bin ^2,3 , SUN Xiaofei ^1,2,3 , FENG Tao ^1,2,3 , ZHANG Yuefei ^1,2,3

1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;
3. Guangzhou Electronic Technology Co. Ltd., Chinese Academy of Sciences, Guangzhou 510070, P.R.China;

Corresponding author：

CHEN Bin, Email: chenbin306@sohu.com

Keywords：

chromosome images recognition; dense convolutional network; segmental recalibration; karyotyping

DOI：

10.7507/1001-5515.201912029

Video：

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Abstract Full text Figures/Tables Video References Cited by

Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome karyotyping. In this paper, we propose a model named segmentally recalibrated dense convolutional network (SR-DenseNet). In each stage of the model, the dense connected network layers is used to extract the features of different abstract levels of chromosomes automatically, and then the concatenation of all the layers which extract different local features is recalibrated with squeeze-and-excitation (SE) block. SE blocks explicitly construct learnable structures for importance of the features. Then a model fusion method is proposed and an expert group of chromosome recognition models is constructed. On the public available Copenhagen chromosome recognition dataset (G-bands) the proposed model achieves error rate of only 1.60%, and with model fusion the error further drops to 0.99%. On the Padova chromosome dataset (Q-bands) the model gets the corresponding error rate of 6.67%, and with model fusion the error further drops to 5.98%. The experimental results show that the method proposed in this paper is effective and has the potential to realize the automation of chromosome type recognition.

Citation： LI Jianming, CHEN Bin, SUN Xiaofei, FENG Tao, ZHANG Yuefei. Banded chromosome images recognition based on dense convolutional network with segmental recalibration. Journal of Biomedical Engineering, 2021, 38(1): 122-130. doi: 10.7507/1001-5515.201912029 Copy

1.	曾秋伊, 朱素优. 绒毛染色体核型分析在产前诊断及自然流产中的临床应用. 中国优生与遗传杂志, 2019, 27(6): 679-681.
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7.	Munot M V. Development of computerized systems for automated chromosome analysis: current status and future prospects. International Journal of Advanced Research in Computer Science, 2018, 9(1): 782-791.
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13.	Hu Jie, Shen Li, Sun Gang. Squeeze-and-Excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 7132-7141.
14.	Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: IEEE, 2017: 4700-4708.
15.	Sharma M, Saha O, Sriraman A, et al. Crowdsourcing for chromosome segmentation and deep classification//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu: IEEE, 2017: 786-793.
16.	Qin Y, Wen J, Zheng H, et al. Varifocal-Net: A chromosome classification approach using deep convolutional networks. IEEE Trans Med Imaging, 2019, 38(11): 2569-2581.
17.	Jindal S, Gupta G, Yadav M, et al. Siamese networks for chromosome classification//2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Honolulu: IEEE, 2017: 72-81.
18.	Gagula-Palalic S, Can M. Human chromosome classification using competitive neural network teams (CNNT) and nearest neighbor//IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia: IEEE, 2014: 626-629.
19.	谭凯. 染色体图像智能分析的综合方法研究及应用. 成都: 电子科技大学, 2020.
20.	Lin C, Zhao G, Yang Z, et al. CIR-net: automatic classification of human chromosome based on Inception-ResNet architecture. IEEE/ACM Trans Comput Biol Bioinform, 2020, PP. DOI: 10.1109/TCBB.2020.3003445.
21.	Poletti E, Grisan E, Ruggeri A. Automatic classification of chromosomes in Q-band images//2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver: IEEE, 2008: 1911-1914.
22.	Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift//International Conference on Machine Learning. Lille: IMLS, 2015: 448-456.
23.	Lundsteen C, Lind A M, Granum E. Visual classification of banded human chromosomes. I. Karyotyping compared with classification of isolated chromosomes. Ann Hum Genet, 1976, 40(1): 87-97.
24.	Sweeney Jr W P. Musavi M T, Guidi J N Classification of chromosomes using a probabilistic neural network. Cytometry: the Journal of the International Society for Analytical Cytology, 1994, 16(1): 17-24.
25.	Piper J, Granum E. On fully automatic feature measurement for banded chromosome classification. Cytometry, 1989, 10(3): 242-255.

1. 曾秋伊, 朱素优. 绒毛染色体核型分析在产前诊断及自然流产中的临床应用. 中国优生与遗传杂志, 2019, 27(6): 679-681.
2. 闫梅, 李辉, 陈佛兰, 等. 广东惠州地区 2117 例孕妇羊水染色体结果分析. 检验医学与临床, 2019, 16(15): 2171-2174.
3. Britto A P, Ravindran G. A review of cytogenetics and its automation. J Med Sci, 2007, 7: 1-18.
4. 郭宏宇, 鲍旭东, 蒋春涛. 基于模糊人工神经网络的染色体识别. 中国生物医学工程学报, 2004, 23(2): 116-120, 126.
5. 蒋欣. 人类染色体图像自动分析技术研究. 武汉: 华中科技大学, 2007.
6. Hu R L, Karnowski J, Fadely R, et al. Image segmentation to distinguish between overlapping human chromosomes. arXiv preprint, 2017. arXiv: 1712.07639.
7. Munot M V. Development of computerized systems for automated chromosome analysis: current status and future prospects. International Journal of Advanced Research in Computer Science, 2018, 9(1): 782-791.
8. Qiu Y, Lu X, Yan S, et al. Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation//Biophotonics and Immune Responses XI. International Society for Optics and Photonics, San Francisco: SPIE, 2016, 9709: 97090K.
9. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks//Advances in Neural Information Processing Systems. Stateline: NIPS, 2012: 1097-1105.
10. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston: IEEE, 2015: 1-9.
11. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition//International Conference on Learning Representations (ICLR), 2015. arXiv: 1409.1556.
12. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016: 770-778.
13. Hu Jie, Shen Li, Sun Gang. Squeeze-and-Excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 7132-7141.
14. Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: IEEE, 2017: 4700-4708.
15. Sharma M, Saha O, Sriraman A, et al. Crowdsourcing for chromosome segmentation and deep classification//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu: IEEE, 2017: 786-793.
16. Qin Y, Wen J, Zheng H, et al. Varifocal-Net: A chromosome classification approach using deep convolutional networks. IEEE Trans Med Imaging, 2019, 38(11): 2569-2581.
17. Jindal S, Gupta G, Yadav M, et al. Siamese networks for chromosome classification//2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Honolulu: IEEE, 2017: 72-81.
18. Gagula-Palalic S, Can M. Human chromosome classification using competitive neural network teams (CNNT) and nearest neighbor//IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia: IEEE, 2014: 626-629.
19. 谭凯. 染色体图像智能分析的综合方法研究及应用. 成都: 电子科技大学, 2020.
20. Lin C, Zhao G, Yang Z, et al. CIR-net: automatic classification of human chromosome based on Inception-ResNet architecture. IEEE/ACM Trans Comput Biol Bioinform, 2020, PP. DOI: 10.1109/TCBB.2020.3003445.
21. Poletti E, Grisan E, Ruggeri A. Automatic classification of chromosomes in Q-band images//2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver: IEEE, 2008: 1911-1914.
22. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift//International Conference on Machine Learning. Lille: IMLS, 2015: 448-456.
23. Lundsteen C, Lind A M, Granum E. Visual classification of banded human chromosomes. I. Karyotyping compared with classification of isolated chromosomes. Ann Hum Genet, 1976, 40(1): 87-97.
24. Sweeney Jr W P. Musavi M T, Guidi J N Classification of chromosomes using a probabilistic neural network. Cytometry: the Journal of the International Society for Analytical Cytology, 1994, 16(1): 17-24.
25. Piper J, Granum E. On fully automatic feature measurement for banded chromosome classification. Cytometry, 1989, 10(3): 242-255.

Journal of Biomedical Engineering

Banded chromosome images recognition based on dense convolutional network with segmental recalibration

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