Skip to main content
SpringerLink
Log in

An automatic progressive chromosome segmentation approach using deep learning with traditional image processing

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The fully automatic chromosome analysis system plays an important role in the detection of genetic diseases, which in turn can reduce the diagnosis burden for cytogenetic experts. Chromosome segmentation is a critical step for such a system. However, due to the non-rigid structure of chromosomes, chromosomes may curve in any direction, and two or more chromosomes may touch or overlap to form unpredictable chromosome clusters in metaphase chromosome images, leading to automatic chromosome segmentation as a challenge. In this paper, we propose an automatic progressive segmentation approach to perform the entire metaphase chromosome image segmentation using deep learning with traditional image processing. It follows three stages. In the first stage, thresholding-based and geometric-based methods are employed to divide all chromosomes as single ones and chromosome clusters. To tackle the segmentation for unpredictable chromosome clusters, we first present a new chromosome cluster identification network named CCI-Net to classify all chromosome clusters into different types in the second stage, and then in the third stage, we combine traditional image processing with deep CNNs to accomplish chromosome instance segmentation from different types of clusters. Evaluation results on a clinical dataset of 1148 metaphase chromosome images show that the proposed automatic progressive segmentation method achieves 94.60% chromosome cluster identification accuracy and 99.15% instance segmentation accuracy. The experimental results exhibit that our proposed approach can effectively identify chromosome clusters and successfully perform fully automatic chromosome segmentation.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Tjio JH, Levan A (1956) The chromosome number in man. Hereditas 42:1–6

    Article  Google Scholar 

  2. Conference D (1960) A proposed standard system of nomenclature of human mitotic chromosomes. Lancet 1:1063–1065

    Google Scholar 

  3. O’Connor C (2008) Karyotyping for chromosomal abnormalities. Nature Educ 1:27

    Google Scholar 

  4. Liu X, Fu L, Lin CW et al (2022) SRAS-net: low-resolution chromosome image classification based on deep learning[J]. IET Syst Biol 16(3–4):85–97

    Article  PubMed  PubMed Central  Google Scholar 

  5. Natarajan AT (2002) Chromosome aberrations: past, present and future. Mutat Res/Fund Mol Mech Mutagen 504(1):3–16

    Article  CAS  Google Scholar 

  6. Patterson D (2009) Molecular genetic analysis of down syndrome. Hum Genet 126(1):195–214

    Article  PubMed  CAS  Google Scholar 

  7. Arora T, Dhir R (2016) A review of metaphase chromosome image selection techniques for automatic karyotype generation. Med Biol Eng Comput 54(8):1147–1157

    Article  PubMed  Google Scholar 

  8. Lerner B (1998) Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans Syst Man Cybern B: Cybern 28(4):544–552

    Article  PubMed  CAS  Google Scholar 

  9. Wang X, Zheng B, Wood M et al (2005) Development and evaluation of automated systems for detection and classification of banded chromosomes. J Phys D Appl Phys 38(15):2536–2542

    Article  CAS  Google Scholar 

  10. Grisan E, Poletti E, Ruggeri A (2009) Automatic segmentation and disentangling of chromosomes in Q-band prometaphase images. IEEE Trans Inf Technol Biomed 13(4):575–581

    Article  PubMed  Google Scholar 

  11. Minaee S, Fotouhi M, Khalaj BH (2014) A geometric approach for fully automatic chromosome segmentation. 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp 1–6

    Google Scholar 

  12. Yilmaz IC, Jie Y, Altinsoy E et al (2018) An improved segmentation for raw G-band chromosome images. 2018 5th International Conference on Systems and Informatics (ICSAI)

    Book  Google Scholar 

  13. Hu RL, Karnowski J, Fadely R, Pommier JP (2017) Image segmentation to distinguish between overlapping human chromosomes. arXiv preprint arXiv:1712.07639

  14. Lin C, Zhao G, Yin A et al (2020) A multi-stages chromosome segmentation and mixed classification method for chromosome automatic karyotyping[C]. In: International Conference on Web Information Systems and Applications. Springer International Publishing, Cham, pp 365–376

  15. Saleh HM, Saad NH, Isa NAM (2019) Overlapping chromosome segmentation using U-Net: convolutional networks with test time augmentation - ScienceDirect. Procedia Comput Sci 159:524–533

    Article  Google Scholar 

  16. Song S, Bai T, Zhao Y et al (2022) A new convolutional neural network architecture for automatic segmentation of overlapping human chromosomes. Neural Process Lett 54(1):285–301

    Article  Google Scholar 

  17. Liu X, Wang S, Lin JCW, Liu S (2022) An algorithm for overlapping chromosome segmentation based on region selection. Neural Comput Applic 1–10

  18. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

    Google Scholar 

  19. Hu J, Shen L, Albanie S et al Squeeze-and-excitation networks. 2018 IEEE/CVF Conf Comput Vis Pattern Recognit 42(2018):2011–2023

  20. Altinsoy E, Yang J, Yilmaz C (2020) Fully-automatic raw G-band chromosome image segmentation. IET Image Process 14(9):1920–1928

    Article  Google Scholar 

  21. Arachchige AS, Samarabandu J, Knoll J et al (2010) An image processing algorithm for accurate extraction of the centerline from human metaphase chromosomes. Image Processing (ICIP), 2010 17th IEEE International Conference on Image Processing, pp 3613–3616

    Google Scholar 

  22. Karvelis PS, Fotiadis DI, Syrrou M et al (2005) Segmentation of chromosome images based on a recursive watershed transform. Third Eur Med Biol Eng Conf 11:1727–1983

    Google Scholar 

  23. Liang JI (1989) Intelligent splitting in the chromosome domain - ScienceDirect. Pattern Recognit 22(5):519–532

    Article  Google Scholar 

  24. Srisang W, Jaroensutasinee K, Jaroensutasinee M (2006) Segmentation of overlapping chromosome images using computational geometry. Walailak J Sci Technol 3(2):181–194

    Google Scholar 

  25. Tanvi T, Dhir R (2014) An efficient segmentation method for overlapping chromosome images. Int J Comput Appl 95(1):29–32

    Google Scholar 

  26. Wayalun P, Chomphuwiset P, Laopracha N et al (2013) Images enhancement of G-band chromosome using histogram equalization, OTSU thresholding, morphological dilation and flood fill techniques. In: Computing and Networking Technology (ICCNT), 2012 8th International Conference on. IEEE, pp 163–168

    Google Scholar 

  27. Lin C, Zhao G, Yin A et al (2021) A novel chromosome cluster types identification method using ResNeXt WSL model. Med Image Anal 69:1–9

    Article  Google Scholar 

  28. He A, Wang K et al (2022) Progressive multi-scale consistent network for multi-class fundus lesion segmentation. 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1–15 4th International Conference on Big Data and Machine Learning (BDML 2021). 2021: 1-10

    Google Scholar 

  29. Chang L, Wu KJ et al (2021) Automatic segmentation of the whole G-band chromosome images based on mask R-CNN and geometric features. 2021 5th International Conference on Advances in Image Processing (ICAIP 2021) and 4th International Conference on Big Data and Machine Learning (BDML 2021), pp 1–10

    Google Scholar 

  30. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 234–241

    Google Scholar 

  31. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision, pp 1026–1034

    Google Scholar 

  32. Huang G, Liu Z, Laurens V et al (2017) Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, pp 4700–4708

    Google Scholar 

  33. Xie S, Girshick R, Dollár P et al (2017) Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1492–1500

    Google Scholar 

  34. He K, Gkioxari G, Dollár P et al (2017) Mask R-CNN. Proceedings of the IEEE international conference on computer vision, pp 2961–2969

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Major Scientific Research Instrument Development Project (6222780062), Science and Technology Commission of Shanghai Municipality under Grant (20142200240), and the National Key Scientific Instruments and Equipment Development Program of China (2013YQ03065101).

Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Ling Chang, Kaijie Wu, Hao Cheng, Chaocheng Gu, Yudi Zhao & Cailian Chen

Authors
  1. Ling Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaijie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaocheng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yudi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cailian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaijie Wu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, L., Wu, K., Cheng, H. et al. An automatic progressive chromosome segmentation approach using deep learning with traditional image processing. Med Biol Eng Comput 62, 207–223 (2024). https://doi.org/10.1007/s11517-023-02896-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02896-x

Keywords

Search

Navigation