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Effective residual convolutional neural network for Chagas disease parasite segmentation

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Abstract

Considered a neglected tropical pathology, Chagas disease is responsible for thousands of deaths per year and it is caused by the parasite Trypanosoma cruzi. Since many infected people can remain asymptomatic, a fast diagnosis is necessary for proper intervention. Parasite microscopic observation in blood samples is the gold standard method to diagnose Chagas disease in its initial phase; however, this is a time-consuming procedure, requires expert intervention, and there is currently no efficient method to automatically perform this task. Therefore, we propose an efficient residual convolutional neural network, named Res2Unet, to perform a semantic segmentation of Trypanosoma cruzi parasites, with an active contour loss and improved residual connections, whose design is based on Heun’s method for solving ordinary differential equations. The model was trained on a dataset of 626 blood sample images and tested on a dataset of 207 images. Validation experiments report that our model achieved a Dice coefficient score of 0.84, a precision value of 0.85, and a recall value of 0.82, outperforming current state-of-the-art methods. Since Chagas disease is a severe and silent illness, our computational model may benefit health care providers to give a prompt diagnose for this worldwide affection.

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Data Availability

The dataset used in this work is available under request to the authors.

Code Availability

The source code is available under request to the authors.

References

  1. World Health Organization. https://www.who.int/en/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis). Accessed: 2020–05–11

  2. Centers for Disease Control and Prevention (2007) Blood donor screening for Chagas disease–United States, 2006–2007. Morb Mortal Wkly Rep (MMWR) 56(07):141–143

    Google Scholar 

  3. Conners EE, Vinetz JM, Weeks JR, Brouwer KC (2016) A global systematic review of Chagas disease prevalence among migrants. Acta Trop 156:68–78. https://doi.org/10.1016/j.actatropica.2016.01.002

    Article  PubMed  PubMed Central  Google Scholar 

  4. World Health Organization. Chagas disease American trypanosomiasis, https://www.who.int/chagas/disease/en/). Accessed: 2020–05–04

  5. Centers for Disease Control and Prevention. Chagas Disease. https://www.cdc.gov/parasites/chagas/. Accessed: 2020–01–05

  6. Ballesteros RG, Martínez CI, Jiménez RT, Antonio CA (2018) Chagas disease: an overview of diagnosis. J Microbiol Experimentation 6:151–157. https://doi.org/10.15406/jmen.2018.06.00207

    Article  Google Scholar 

  7. Anez N, Carrasco H, Parada H et al (1999) Acute Chagas’ disease in western Venezuela: a clinical, seroparasitologic, and epidemiologic study. Am J Trop Med Hyg 60(2):215–222

    Article  CAS  Google Scholar 

  8. Kirchhoff LV, Votava JR, Ochs DE et al (1996) Comparison of PCR and microscopic methods for detecting Trypanosoma cruzi. J Clin Microbiol 34(5):1171–1175

    Article  CAS  Google Scholar 

  9. Storino R (2002) Consenso de enfermedad de Chagas, Topico I: Enfermedad de Chagas con parasitemia evidente. Rev Arg Cardiol 70(1):15–39

    Google Scholar 

  10. Bern C (2015) Chagas’ disease. N Engl J Med 373:456–466

    Article  CAS  Google Scholar 

  11. Uc-Cetina V, Brito-Loeza C, Ruiz-Piña H (2013) Chagas parasites detection through gaussian discriminant analysis. Abstraction Appl 8:6–17

    Google Scholar 

  12. Soberanis-Mukul R, Uc-Cetina V, Brito-Loeza C, Ruiz-Piña H (2013) An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images. Comput Methods Programs Biomed 112(3):633–639. https://doi.org/10.1016/j.cmpb.2013.07.013

    Article  PubMed  Google Scholar 

  13. Soberanis-Mukul R (2014) Algoritmos de segmentación de Trypanosoma cruzi en imágenes de muestras sanguineas, Master’s thesis, Universidad Autónoma de Yucatán

  14. Uc-Cetina V, Brito-Loeza C, Ruiz-Piña H (2015) Chagas parasite detection in blood images using adaboost. Comput Math Methods Med 2015:1–13. https://doi.org/10.1155/2015/139681

    Article  Google Scholar 

  15. Latif J, Xiao C, Imran A, Tu S (2019) Medical imaging using machine learning and deep learning algorithms: a review. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), p 1–5. https://doi.org/10.1109/ICOMET.2019.8673502

  16. Kamal-Alsheref F, Hassan W (2019) Blood diseases detection using classical machine learning algorithms. Int J Adv Comput Sci Appl 10. https://doi.org/10.14569/IJACSA.2019.0100712

  17. Chen X, Williams B, Vallabhaneni S, Czanner G, Williams R, Zheng Y (2019) Learning active contour models for medical image segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2019.01190

  18. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, vol. 9351, pp. 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  19. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90

  20. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In 2015 International Conference on Learning Representations (ICLR)

  21. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2015.7298594

  22. M. Telgarsky (2016) Benefits of depth in neural networks, JMLR: Workshop and Conference Proceedings 49:1–23

  23. Zhang K, Sun M, Han T, Yuan X, Guo L, Liu T (2018) Residual networks of residual networks: multilevel residual networks. In IEEE Trans Cir Syst Video Technol 28(6): 1303-1314. https://doi.org/10.1109/TCSVT.2017.2654543

  24. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. Comp Vis – ECCV 2016 9908. https://doi.org/10.1007/978-3-319-46493-0_38

  25. Zhang Z, Liu Q (2017) Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2018.2802944

    Article  Google Scholar 

  26. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications. https://doi.org/10.1007/978-3-319-46976-8_19

  27. Haber E, Ruthotto L (2017) Stable architectures for deep neural networks. Inverse Prob 34:014004. https://doi.org/10.1088/1361-6420/aa9a90

    Article  Google Scholar 

  28. Lu Y, Zhong A, Li Q, Dong B (2018) Beyond finite layer neural networks: bridging deep architectures and numerical differential equations. In 35th International Conference on Machine Learning, ICML 2018, vol. 7, pp. 5181–5190. Taken from https://arxiv.org/abs/1710.10121. Accessed 5 May 2020

  29. Sauer T (2018) Numerical analysis, 3rd edition. Pearson, Hoboken, New Jersey

  30. Chen R, Rubanova Y, Bettencourt J, Duvenaud D (2018) Neural ordinary differential equations. In 32nd Conference on Neural Information Processing Systems. Taken from https://arxiv.org/abs/1806.07366. Accessed 2020/01/07

  31. Bengio Y, Simard P, Frasconi D (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5:157–166. https://doi.org/10.1109/72.279181

    Article  CAS  PubMed  Google Scholar 

  32. Süli E, Mayers D (2003) An introduction to numerical analysis. Cambridge University Press, New York

  33. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437

    Article  Google Scholar 

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Funding

This scientific work was partly supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) of Mexico.

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Authors and Affiliations

  1. Computational Learning and Imaging Research (CLIR), Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tab. Cat. 13615, Merida, 97119, Mexico

    Allan Ojeda-Pat, Anabel Martin-Gonzalez, Carlos Brito-Loeza & Daniel Ruz-Suarez

  2. Centro de Investigaciones Regionales, Universidad Autónoma de Yucatán, Merida, 97000, Mexico

    Hugo Ruiz-Piña

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  1. Allan Ojeda-Pat
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  2. Anabel Martin-Gonzalez
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  3. Carlos Brito-Loeza
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Correspondence to Anabel Martin-Gonzalez.

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Ojeda-Pat, A., Martin-Gonzalez, A., Brito-Loeza, C. et al. Effective residual convolutional neural network for Chagas disease parasite segmentation. Med Biol Eng Comput 60, 1099–1110 (2022). https://doi.org/10.1007/s11517-022-02537-9

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  • DOI: https://doi.org/10.1007/s11517-022-02537-9

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