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A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes

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Abstract

In clinical diagnosis, karyotyping is carried out to detect genetic disorders due to chromosomal aberrations. Accurate segmentation is crucial in this process that is mostly operated by experts. However, it is time-consuming and labor-intense to segment chromosomes and their overlapping regions. In this research, we look into the automatic segmentation of overlapping pairs of chromosomes. Different from standard semantic segmentation applications that mostly detect object regions or boundaries, this study attempts to predict not only non-overlapping regions but also the order of superposition and opaque regions of the underlying chromosomes. We propose a novel convolutional neural network called Compact Seg-UNet with enhanced deep feature learning capability and training efficacy. To address the issue of unrealistic images in use characterized by overlapping regions of higher color intensities, we propose a novel method to generate more realistic images with opaque overlapping regions. On the segmentation performance of overlapping chromosomes for this new dataset, our Compact Seg-UNet model achieves an average IOU score of 93.44% ± 0.26 which is significantly higher than the result of a simplified U-Net reported by literature by around 6.08%. The corresponding F1 score also increases from \(0.9262\pm 0.1188\) to \(0.9596 \pm 0.0814\).

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References

  1. Abdulnabi AH, Wang G, Jiwen L, Jia K (2015) Multi-task CNN model for attribute prediction. IEEE Trans Multimedia 17(11):1949–1959

    Article  Google Scholar 

  2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2014) Chapter 4. Molecular biology of the cell, 6th edn. Garland Science

  3. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  4. Chen Y, Lin G, Li S, Bourahla O, Wu Y, Wang F, Feng J, Xu M, Li X (2020) BANet: bidirectional aggregation network with occlusion handling for panoptic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3793–3802

  5. Chung M, Lee J, Lee M, Lee J, Shin Y-G (2020) Deeply self-supervised contour embedded neural network applied to liver segmentation. Comput Methods Programs Biomed 192:105447

  6. Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414–2423

  7. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  8. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  9. Gupta G, Yadav M, Sharma M, Vig L et al (2017) Siamese networks for chromosome classification. In: 2017 IEEE international conference on computer vision workshop (ICCVW), pp 72–81

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

  11. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV), pp 2980–2988

  12. Hu RL, Karnowski J, Fadely R, Pommier J-P (2017) Image segmentation to distinguish between overlapping human chromosomes. In: 2017 Machine Learning for Health (NIPS). Long Beach, CA

  13. Izmailov P, Podoprikhin D, Garipov T, Vetrov D, Wilson AG (2018) Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407

  14. Karvelis P, Likas A, Fotiadis DI (2010) Identifying touching and overlapping chromosomes using the watershed transform and gradient paths. Pattern Recogn Lett 31(16):2474–2488

    Article  Google Scholar 

  15. King RC (1974) A dictionary of genetics: Rev. Oxford University Press, Oxford

    Google Scholar 

  16. Kowal M, Żejmo M, Skobel M, Korbicz J, Monczak R (2020) Cell nuclei segmentation in cytological images using convolutional neural network and seeded watershed algorithm. J Digit Imaging 33(1):231–242

    Article  Google Scholar 

  17. Lazarow J, Lee K, Shi K, Tu Z (2020) Learning instance occlusion for panoptic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10720–10729

  18. Lee C, John Iafrate A, Brothman AR (2007) Copy number variations and clinical cytogenetic diagnosis of constitutional disorders. Nat Genet 39:48

    Article  Google Scholar 

  19. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  20. MacLeod RAF, Kaufmann M, Drexler HG (2011) Cytogenetic analysis of cancer cell lines. In: Cancer cell culture. Springer, Berlin, pp 57–78

  21. Manohar R, Gawande J (2017) Watershed and clustering based segmentation of chromosome images. In: 2017 IEEE 7th international advance computing conference (IACC), pp 697–700

  22. Marshall CR, Noor A, Vincent JB, Lionel AC, Feuk L, Skaug J, Shago M, Moessner R, Pinto D, Ren Y et al (2008) Structural variation of chromosomes in autism spectrum disorder. Am J Human Genet 82(2):477–488

    Article  Google Scholar 

  23. Minaee S, Fotouhi M, Khalaj BH (2014) A geometric approach to fully automatic chromosome segmentation. In: Signal processing in medicine and biology symposium (SPMB), 2014 IEEE, pp 1–6

  24. Morris DD (2018) A pyramid CNN for dense-leaves segmentation. In: 2018 15th conference on computer and robot vision (CRV), pp 238–245. https://doi.org/10.1109/CRV.2018.00041

  25. Munné S, Alikani M, Tomkin G, Grifo J, Cohen J (1995) Embryo morphology, developmental rates, and maternal age are correlated with chromosome abnormalities. Fertil Steril 64(2):382–391

    Article  Google Scholar 

  26. Munot MV, Mukherjee J, Joshi M (2013) A novel approach for efficient extrication of overlapping chromosomes in automated karyotyping. Med Biol Eng Comput 51(12):1325–1338

    Article  Google Scholar 

  27. Ranjan R, Arachchige AS, Samarabandu J, Rogan PK, Knoll J (2012) Automatic detection of pale path and overlaps in chromosome images using adaptive search technique and re-thresholding. In: VISAPP (1), pp 462–466

  28. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28: 91–99

  29. Roizen NJ, Patterson D (2003) Down’s syndrome. Lancet 361(9365):1281–1289

  30. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241

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

    Article  Google Scholar 

  32. Sharma M, Saha O, Sriraman A, Hebbalaguppe R, Vig L, Karande S (2017) Crowdsourcing for chromosome segmentation and deep classification. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 786–793

  33. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556

  34. Srisang W, Jaroensutasinee K, Jaroensutasinee M (2011) Segmentation of overlapping chromosome images using computational geometry. Walailak J Sci Technol (WJST) 3(2):181–194

    Google Scholar 

  35. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  36. Tassabehji M, Metcalfe K, Karmiloff-Smith A, Carette MJ, Grant J, Nick Dennis W, Reardon MS, Read AP, Donnai D (1999) Williams syndrome: use of chromosomal microdeletions as a tool to dissect cognitive and physical phenotypes. Am J Human Genet 64(1):118–125

    Article  Google Scholar 

  37. Wan TSK (2014) Cancer cytogenetics: methodology revisited. Ann Lab Med 34(6):413–425

    Article  Google Scholar 

  38. Wu B, Liu Z, Yuan Z, Sun G, Wu C (2017) Reducing overfitting in deep convolutional neural networks using redundancy regularizer. In: International conference on artificial neural networks. Springer, Berlin, pp 49–55

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Acknowledgements

This study is supported by the National Science and Foundation of China (NSFC) under Grant 61501380, Key Program Special Fund in XJTLU (KSF-T-01), the Key Program Special Fund in XJTLU (KSF-A-22), the Key Programme Special Fund in XJTLU (KSF-E-21), Jiangsu Society and Science Development Program, Project No. BE2016678, and the Open Program of Neusoft corporation, item number SKLSAOP1702.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Sifan Song, Yanxin Zhao & Wenbo Zhang

  2. School of Mathematics, The University of Edinburgh, Edinburgh EH9 3JW, UK

    Tianming Bai

  3. Suzhou Sano Precision Medicine Ltd., Suzhou, 215123, China

    Chunxiao Yang

  4. Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Jia Meng

  5. Department of Applied Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Fei Ma

  6. School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Jionglong Su

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  1. Sifan Song
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  2. Tianming Bai
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  3. Yanxin Zhao
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  4. Wenbo Zhang
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  5. Chunxiao Yang
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  6. Jia Meng
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  7. Fei Ma
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Correspondence to Fei Ma or Jionglong Su.

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Song, S., Bai, T., Zhao, Y. et al. A New Convolutional Neural Network Architecture for Automatic Segmentation of Overlapping Human Chromosomes. Neural Process Lett 54, 285–301 (2022). https://doi.org/10.1007/s11063-021-10629-0

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  • DOI: https://doi.org/10.1007/s11063-021-10629-0

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