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Development of New Staining Procedures for Diagnosing Cryptosporidium spp. in Fecal Samples by Computerized Image Analysis

Published online by Cambridge University Press:  15 October 2021

Saulo Hudson Nery Loiola ,
Felipe Lemes Galvão ,
Bianca Martins dos Santos ,
Stefany Laryssa Rosa ,
Felipe Augusto Soares ,
Sandra Valéria Inácio ,
Celso Tetsuo Nagase Suzuki ,
Edvaldo Sabadini ,
Katia Denise Saraiva Bresciani  and
Alexandre Xavier Falcão
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Saulo Hudson Nery Loiola*
Affiliation:
School of Medical Sciences, University of Campinas, 126 Tessália Vieira de Camargo St., Campinas, São Paulo 13083-887, Brazil
Felipe Lemes Galvão
Affiliation:
University of Campinas, Institute of Computing, 573, IC-3,5 Saturnino de Brito St., Room 364, Campinas, São Paulo 13083-852, Brazil
Bianca Martins dos Santos
Affiliation:
School of Medical Sciences, University of Campinas, 126 Tessália Vieira de Camargo St., Campinas, São Paulo 13083-887, Brazil
Stefany Laryssa Rosa
Affiliation:
School of Medical Sciences, University of Campinas, 126 Tessália Vieira de Camargo St., Campinas, São Paulo 13083-887, Brazil
Felipe Augusto Soares
Affiliation:
School of Medical Sciences, University of Campinas, 126 Tessália Vieira de Camargo St., Campinas, São Paulo 13083-887, Brazil
Sandra Valéria Inácio
Affiliation:
School of Veterinary Medicine, São Paulo State University (UNESP), 793 Clóvis Pestana St., Araçatuba, São Paulo 16050-680, Brazil
Celso Tetsuo Nagase Suzuki
Affiliation:
University of Campinas, Institute of Computing, 573, IC-3,5 Saturnino de Brito St., Room 364, Campinas, São Paulo 13083-852, Brazil
Edvaldo Sabadini
Affiliation:
University of Campinas, Institute of Chemistry, 126 Josué de Castro St., Campinas, São Paulo 13083-861, Brazil
Katia Denise Saraiva Bresciani
Affiliation:
School of Veterinary Medicine, São Paulo State University (UNESP), 793 Clóvis Pestana St., Araçatuba, São Paulo 16050-680, Brazil
Alexandre Xavier Falcão
Affiliation:
University of Campinas, Institute of Computing, 573, IC-3,5 Saturnino de Brito St., Room 364, Campinas, São Paulo 13083-852, Brazil
Jancarlo Ferreira Gomes
Affiliation:
School of Medical Sciences, University of Campinas, 126 Tessália Vieira de Camargo St., Campinas, São Paulo 13083-887, Brazil University of Campinas, Institute of Computing, 573, IC-3,5 Saturnino de Brito St., Room 364, Campinas, São Paulo 13083-852, Brazil
*
*Corresponding author: Saulo Hudson Nery Loiola, E-mail: [email protected]
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Abstract

Interpretation errors may still represent a limiting factor for diagnosing Cryptosporidium spp. oocysts with the conventional staining techniques. Humans and machines can interact to solve this problem. We developed a new temporary staining protocol associated with a computer program for the diagnosis of Cryptosporidium spp. oocysts in fecal samples. We established 62 different temporary staining conditions by studying 20 experimental protocols. Cryptosporidium spp. oocysts were concentrated using the Three Fecal Test (TF-Test®) technique and confirmed by the Kinyoun method. Next, we built a bank with 299 images containing oocysts. We used segmentation in superpixels to cluster the patches in the images; then, we filtered the objects based on their typical size. Finally, we applied a convolutional neural network as a classifier. The trichrome modified by Melvin and Brooke, at a concentration use of 25%, was the most efficient dye for use in the computerized diagnosis. The algorithms of this new program showed a positive predictive value of 81.3 and 94.1% sensitivity for the detection of Cryptosporidium spp. oocysts. With the combination of the chosen staining protocol and the precision of the computational algorithm, we improved the Ova and Parasite exam (O&P) by contributing in advance toward the automated diagnosis.

Keywords

Cryptosporidium spp.fecesmachine learningoocystsstain
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 27 , Issue 6 , December 2021 , pp. 1518 - 1528
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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References

Ahmed, SA & Karanis, P (2018). Comparison of current methods used to detect Cryptosporidium oocysts in stools. Int J Hyg Environ Health 221, 743–763.CrossRefGoogle ScholarPubMed
Alva, A, Cangalaya, C, Quiliano, M, Krebs, C, Gilman, RH, Sheen, P & Zimic, M (2017). Mathematical algorithm for the automatic recognition of intestinal parasites. PLoS ONE 12, e0175646.CrossRefGoogle ScholarPubMed
Brar, APS, Sood, NK, Singla, LD, Kaur, P, Gupta, K & Sandhu, BS (2017). Validation of Romanowsky staining as a novel screening test for the detection of faecal cryptosporidial oocysts. J Parasit Dis 41, 260–262.CrossRefGoogle ScholarPubMed
Casemore, DP (1991). ACP broadsheet 128: June 1991. Laboratory methods for diagnosing cryptosporidiosis. J Clin Pathol 44, 445–451.CrossRefGoogle ScholarPubMed
Castañón, CAB, Fraga, JS, Fernandez, S, Gruber, A & Costa, LF (2007). Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria. Pattern Recognit 40, 1899–1910.CrossRefGoogle Scholar
Chalmers, RM, Atchison, C, Barlow, K, Young, Y, Roche, A & Manuel, R (2015). An audit of the laboratory diagnosis of cryptosporidiosis in England and Wales. J Med Microbiol 64, 688–693.CrossRefGoogle ScholarPubMed
Current, WL & Garcia, LS (1991). Cryptosporidiosis. Clin Microbiol Rev 4, 325–358.CrossRefGoogle ScholarPubMed
Deng, J, Dong, W, Socher, R, Li, L-J, Li, K & Fei-Fei, L (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE.CrossRefGoogle Scholar
DuPont, HL (2016). Persistent diarrhea: A clinical review. JAMA 315, 2712–2723.CrossRefGoogle ScholarPubMed
Elwin, K, Hadfield, SJ, Robinson, G & Chalmers, RM (2012). The epidemiology of sporadic human infections with unusual cryptosporidia detected during routine typing in England and Wales, 2000-2008. Epidemiol Infect 140, 673–683.CrossRefGoogle Scholar
Garcia, LS, Arrowood, M, Kokoskin, E, Paltridge, GP, Pillai, DR, Procop, GW, Ryan, N, Shimizu, RY & Visvesvara, G (2018). Practical guidance for clinical microbiology laboratories: Laboratory diagnosis of parasites from the gastrointestinal tract. Clin Microbiol Rev 31, e00025-17.CrossRefGoogle ScholarPubMed
Garcia, LS, Brewer, T, Brukner, D & Shimizu, R (1982). Clinical laboratory diagnosis of Cryptosporidium from human fecal specimens. Clin Microbiol News 4, 136–137.CrossRefGoogle Scholar
Gerace, E, Lo Presti, VDM & Biondo, C (2019). Cryptosporidium infection: Epidemiology, pathogenesis, and differential diagnosis. Eur J Microbiol Immunol 9, 119–123.CrossRefGoogle ScholarPubMed
Gomes, JF, Hoshino-Shimizu, S, Dias, LCS, Araujo, AJSA, Castilho, VLP & Neves, FAMA (2004). Evaluation of a novel kit (TF-Test) for the diagnosis of intestinal parasitic infections. J Clin Lab Anal 18, 132–138.CrossRefGoogle Scholar
Gonzalez, RC & Woods, RE (2018). Digital Image Processing, 4th ed. New York, USA: Pearson.Google Scholar
Heine, J (1982). Eine einfache Nachweismethode für Kryptosporidien im Kot. Zentralbl Veterinärmed, Reihe B 29, 324–327.CrossRefGoogle Scholar
Henriksen, SA & Pohlenz, JF (1981). Staining of cryptosporidia by a modified Ziehl-Neelsen technique. Acta Vet Scand 22, 594–596.CrossRefGoogle ScholarPubMed
Holmström, O, Linder, N, Ngasala, B, Mårtensson, A, Linder, E, Lundin, M, Moilanen, H, Suutala, A, Diwan, V & Lundin, J (2017). Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium. Glob Health Action 10, 1337325.CrossRefGoogle ScholarPubMed
Horen, WP (1983). Detection of Cryptosporidium in human fecal specimens. J Parasitol 69, 622–624.CrossRefGoogle ScholarPubMed
Inacio, SV, Gomes, JF, Oliveira, BCM, Falcao, AX, Suzuki, CTN, Dos Santos, BM, de Aquino, MCC, de Paula Ribeiro, RS, de Assuncao, DM, Casemiro, PAF, Meireles, MV & Bresciani, KDS (2016). Validation of a new technique to detect Cryptosporidium spp. oocysts in bovine feces. Prev Vet Med 134, 1–5.CrossRefGoogle ScholarPubMed
Khanna, V, Tilak, K, Ghosh, A & Mukhopadhyay, C (2014). Modified negative staining of Heine for fast and inexpensive screening of Cryptosporidium, Cyclospora, and Cystoisospora spp. Int Scholarly Res Not 2014, 165424.Google ScholarPubMed
Ma, P & Soave, R (1983). Three-step stool examination for cryptosporidiosis in 10 homosexual Men with protracted watery diarrhea. J Infect Dis 147, 824–828.CrossRefGoogle ScholarPubMed
Manser, M, Granlund, M, Edwards, H, Saez, A, Petersen, E, Evengard, B & Chiodini, P (2014). Detection of Cryptosporidium and Giardia in clinical laboratories in Europe—A comparative study. Clin Microbiol Infect 20, O65–O71.CrossRefGoogle ScholarPubMed
McHardy, IH, Wu, M, Shimizu-Cohen, R, Couturier, MR & Humphries, RM (2014). Detection of intestinal protozoa in the clinical laboratory. J Clin Microbiol 52, 712–720.CrossRefGoogle ScholarPubMed
Melvin, DM, Brooke, MM & Centers for Disease Control (U.S.) (1985). Laboratory Procedures for the Diagnosis of Intestinal Parasites. Atlanta, GA: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, Laboratory Improvement Program Office, Laboratory Training and Consultation Division.Google Scholar
Polage, C, Stoddard, GJ, Rolfs, RT & Petti, CA (2011). Physician use of parasite tests in the United States from 1997 to 2006 and in a Utah Cryptosporidium outbreak in 2007. J Clin Microbiol 49, 591–596.CrossRefGoogle Scholar
Potters, I & van Esbroeck, M (2010). Negative staining technique of Heine for the detection of Cryptosporidium spp.: A fast and simple screening technique. Open Parasitol J 4, 1–4.CrossRefGoogle Scholar
Ren X & Malik J (2003). Learning a classification model for segmentation. In Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 10–17.Google Scholar
Ryan, U, Fayer, R & Xiao, L (2014). Cryptosporidium species in humans and animals: Current understanding and research needs. Parasitology 141, 1667–1685.CrossRefGoogle ScholarPubMed
Simonyan, K & Zisserman, A (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs].Google Scholar
Slapeta, J (2017). Cryptosporidium: Identification and genetic typing. Curr Protoc Microbiol 44, 20B.1.1–20B.1.17.CrossRefGoogle ScholarPubMed
Suzuki, CTN, Gomes, JF, Falcão, AX, Shimizu, SH & Papa, JP (2013). Automated diagnosis of human intestinal parasites using optical microscopy images. In 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 460–463.CrossRefGoogle Scholar
Tyzzer, EE (1910). An extracellular coccidium, Cryptosporidium muris (Gen. Et Sp. Nov.), of the gastric glands of the common mouse. J Med Res 23, 487–510.3.Google Scholar
Vargas-Munoz, JE, Chowdhury, AS, Alexandre, EB, Galvao, FL, Vechiatto Miranda, PA & Falcao, AX (2019). An iterative spanning forest framework for superpixel segmentation. IEEE Trans Image Process 28, 3477–3489.CrossRefGoogle ScholarPubMed
Weber, R, Bryan, RT & Juranek, DD (1992). Improved stool concentration procedure for detection of Cryptosporidium oocysts in fecal specimens. J Clin Microbiol 30, 2869–2873.CrossRefGoogle ScholarPubMed
Wheatley, WB (1951). A rapid staining procedure for intestinal amoebae and flagellates. Am J Clin Pathol 21, 990–991.CrossRefGoogle ScholarPubMed
World Health Organization (1991). Basic laboratory methods in medical parasitology. WHO. Available at https://www.who.int/malaria/publications/atoz/9241544104_part1/en/Google Scholar
World Health Organization (2013). Ending preventable child deaths from pneumonia and diarrhoea by 2025. Available at http://www.who.int/maternal_child_adolescent/documents/global_action_plan_pneumonia_diarrhoea/en/Google Scholar
World Health Organization (2019). World Health Statistics 2019: Monitoring health for the SDGs. Available at http://www.who.int/gho/publications/world_health_statistics/2019/en/ (accessed January 24, 2020).Google Scholar
Zhang, J, Lin, Y, Liu, Y, Li, Z, Li, Z, Hu, S, Liu, Z, Lin, D & Wu, Z (2014). Cascaded-automatic segmentation for Schistosoma japonicum eggs in images of fecal samples. Comput Biol Med 52, 18–27.CrossRefGoogle ScholarPubMed
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