Comparison of Digital Image Analysis and Conventional Microscopy in Evaluating Erythrocyte Morphology in Peripheral Blood Smears
DOI:
https://doi.org/10.21141/PJP.2020.02Keywords:
erythrocyte morphology, digital imaging, microscopyAbstract
Background and Objectives. The use of conventional microscopy still forms the basis for the morphologic evaluation of erythrocytes despite widespread use of automated tests in the hematology laboratory. This requires a considerable length of time and expertise, and have the potential of becoming a source of errors and delay in reporting. Advances in image processing and machine learning in recent years have shown acceptable performance characteristics and have promising applications in the diagnostic laboratory. Use of these newly-developed technologies can address the stated problems and provide an alternative approach in the microscopic analysis of erythrocytes.
Methodology. This prospective validation study compared digital image analysis using a machine-learning based image recognition algorithm with conventional microscopy performed by a trained microscopist, which served as the reference standard. Random deidentified anticoagulated peripheral blood samples submitted to the hematology laboratory were assessed.
Results. A total of 956 erythrocytes were evaluated after image processing using support vector machine and routine microscopy as classifiers of erythrocytes into three categories: size, central pallor, and shape. The tested software was able to achieve a strong level of agreement compared to conventional microscopy, having kappa values ranging from 0.81 to 0.86. Accuracy for size, central pallor and shape were 89.88%, 93.72% and 87.89%, respectively.
Conclusion. The validated image recognition software is an acceptable diagnostic test in determining erythrocyte morphology in peripheral blood smears. Its integration can potentially minimize hands-on time and improve the diagnostic laboratory workflow.
Registration. Philippine Health Research Registry (PHRR) ID: PHRR191211-002348; University of the Philippines Manila Research Ethics Board (UPMREB): 2019-356-01.
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References
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24 Lee H, Chen YP. Cell morphology based classification for red cells in blood smear images. Pattern Recognit Lett. 2014;49:155-61. https://doi.org/10.1016/j.patrec.2014.06.010.
25 Kim G, Jo Y, Cho H, Min HS, Park Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosensors and Bioelectronics. 2019;123:69-76. https://www.ncbi.nlm.nih.gov/pubmed/30321758. https://doi.org/10.1016/j.bios.2018.09.068.
26 Kratz A, Lee SH, Zini G, et al. Digital morphology analyzers in hematology: ICSH review and recommendations. International journal of laboratory hematology. 2019;41(4):437-47. https://www.ncbi.nlm.nih.gov/pubmed/31046197. https://doi.org/10.1111/ijlh.13042.
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28 Bigorra L, Merino A, Alférez S, Rodellar J. Feature analysis and automatic identification of leukemic lineage blast cells and reactive lymphoid cells from peripheral blood cell images. J Clin Laboratory Anal. 2017;31(2):e22024. https://doi.org/10.1002/jcla.22024.
29 Rodellar J, Alférez S, Acevedo A, Molina A, Merino A. Image processing and machine learning in the morphological analysis of blood cells. Int J Lab Hematol. 2018;40(Suppl 1):46-53. https://www.ncbi.nlm.nih.gov/pubmed/29741258. https://doi.org/10.1111/ijlh.12818.
30 Winkelman JW, Tanasijevic MJ, Zahniser DJ. A novel automated slide-based technology for visualization, counting, and characterization of the formed elements of blood: a proof of concept study. Arch Pathol & Lab Med. 2017;141(8):1107-12. https://www.ncbi.nlm.nih.gov/pubmed/28421831. https://doi.org/10.5858/arpa.2016-0633-OA.
2 de Vet HC, Koudstaal J, Kwee WS, Willebrand D, Arends JW. Efforts to improve interobserver agreement in histopathological grading. J Clin Epidemiol. 1995;48(7):869-73. https://www.ncbi.nlm.nih.gov/pubmed/7782794. https://doi.org/10.1016/0895-4356(94)00225-f.
3 Ford J. Red blood cell morphology. Int J Lab Hematol. 2013;35(3):351-7. https://www.ncbi.nlm.nih.gov/pubmed/23480230. https://doi.org/10.1111/ijlh.12082.
4 Pierre RV. Red cell morphology and the peripheral blood film. Clin Lab Med. 2002;22(1):25-61. https://www.ncbi.nlm.nih.gov/pubmed/11933577. https://doi.org/10.1016/s0272-2712(03)00066-0.
5 Fred HL. Maxwell Myer Wintrobe: new history and a new appreciation. Tex Heart Inst J. 2007;34(3):328-35. https://www.ncbi.nlm.nih.gov/pubmed/17948084. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1995040.
6 Hur M, CHO JH, Kim H, HONG MH, MOON HW, YUN YM, Kim JQ. Optimization of laboratory workflow in clinical hematology laboratory with reduced manual slide review: comparison between Sysmex XE‐2100 and ABX Pentra DX120. Int J Lab Hematol. 2011;33(4):434-40. https://www.ncbi.nlm.nih.gov/pubmed/21418541.
https://doi.org/10.1111/j.1751-553X.2011.01306.x.
7 Palmer L, Briggs C, McFadden S, et al. CSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features. Int J Lab Hematol. 2015;37(3):287-303. https://www.ncbi.nlm.nih.gov/pubmed/25728865. https://doi.org/10.1111/ijlh.12327.
8 Comar SR, Malvezzi M, Pasquini R. Evaluation of criteria of manual blood smear review following automated complete blood counts in a large university hospital. Rev Bras Hematol Hemoter. 2017;39(4):306-17. https://www.ncbi.nlm.nih.gov/pubmed/29150102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5693276. https://doi.org/10.1016/j.bjhh.2017.06.007.
9 Flax S. Why do we still need to evaluate peripheral blood smears? American Association for Clinical Chemistry [Internet]. 2017 Jun [cited 2019 Apr 15]. Available from: https://www.aacc.org/publications/cln/articles/2017/june/why-do-we-still-need-to-evaluate-peripheral-blood-smears.
10 Mohammed EA, Mohamed MM, Far BH, Naugler C. Peripheral blood smear image analysis: a comprehensive review. J Pathol inform. 2014;5(1):9. https://www.ncbi.nlm.nih.gov/pubmed/24843821. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023032. https://doi.org/10.4103/2153-3539.129442.
11 Kim K, Jeon J, Choi W, Kim P, Ho YS. Automatic cell classification in human’s peripheral blood images based on morphological image processing. In: Stumptner M, Corbett D, Brooks M, eds. AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science, vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_20.
12 Chen S, Zhao M, Wu G, Yao C, Zhang J. Recent advances in morphological cell image analysis. Comput Math Methods Med. 2012;2012. https://www.ncbi.nlm.nih.gov/pubmed/22272215. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261466. https://doi.org/10.1155/2012/101536.
13 Jordan MI, Mitchell TM2. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255-60. https://www.ncbi.nlm.nih.gov/pubmed/26185243. https://doi.org/10.1126/science.aaa8415.
14 Cabitza F, Banfi G. Machine learning in laboratory medicine: waiting for the flood?. Clin Chem Lab Med. 2018;56(4):516-24. https://www.ncbi.nlm.nih.gov/pubmed/29055936. https://doi.org/10.1515/cclm-2017-0287.
15 Gunčar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8(1):411. https://www.ncbi.nlm.nih.gov/pubmed/29323142. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765139. https://doi.org/10.1038/s41598-017-18564-8.
16 Ghosh S, Roy A, Sarkar D. Assessment of morphologically altered rbcs using image processing tools. Materials today: proceedings. 2016;3(10 part A):3361-6. https://doi.org/10.1016/j.matpr.2016.10.017.
17 Kraus OZ, Grys BT, Ba J, et al. Automated analysis of high‐content microscopy data with deep learning. Mol Syst Biol. 2017;13(4):924. https://www.ncbi.nlm.nih.gov/pubmed/28420678. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408780. https://doi.org/10.15252/msb.20177551.
18 Adollah R, Mashor MY, Nasir NM, Rosline H, Mahsin H, Adilah H. Blood cell image segmentation: a review. In: 4th Kuala Lumpur international conference on biomedical engineering. Springer, Berlin, Heidelberg; 2008, pp. 141-144.
19 Divina P, Felices J. Identification of abnormal red blood cells and diagnosing specific types of anemia using image processing and support vector machine. Mapua University, Philippines [Unpublished].
20 McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276-82. https://www.ncbi.nlm.nih.gov/pubmed/23092060. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900052.
21 Tomari R, Zakaria WN, Jamil MM, Nor FM, Fuad NF. Computer aided system for red blood cell classification in blood smear image. Procedia Comput Sci. 2014;42:206-13. https://doi.org/10.1016/j.procs.2014.11.053.
22 Dalvi PT, Vernekar N. Computer aided detection of abnormal red blood cells. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India; 2016.
23 Syahputra MF, Sari, AR, Rahmat RF. Abnormality classification on the shape of red blood cells using radial basis function network. 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali; 2017.
24 Lee H, Chen YP. Cell morphology based classification for red cells in blood smear images. Pattern Recognit Lett. 2014;49:155-61. https://doi.org/10.1016/j.patrec.2014.06.010.
25 Kim G, Jo Y, Cho H, Min HS, Park Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosensors and Bioelectronics. 2019;123:69-76. https://www.ncbi.nlm.nih.gov/pubmed/30321758. https://doi.org/10.1016/j.bios.2018.09.068.
26 Kratz A, Lee SH, Zini G, et al. Digital morphology analyzers in hematology: ICSH review and recommendations. International journal of laboratory hematology. 2019;41(4):437-47. https://www.ncbi.nlm.nih.gov/pubmed/31046197. https://doi.org/10.1111/ijlh.13042.
27 Barghout L. Spatial-taxon information granules as used in iterative fuzzy-decision-making for image segmentation. In: Pedrycz W., Chen SM. (eds) Granular Computing and Decision-Making. Studies in Big Data, vol 10. Springer, Cham; 2015. https://doi.org/10.1007/978-3-319-16829-6_12.
28 Bigorra L, Merino A, Alférez S, Rodellar J. Feature analysis and automatic identification of leukemic lineage blast cells and reactive lymphoid cells from peripheral blood cell images. J Clin Laboratory Anal. 2017;31(2):e22024. https://doi.org/10.1002/jcla.22024.
29 Rodellar J, Alférez S, Acevedo A, Molina A, Merino A. Image processing and machine learning in the morphological analysis of blood cells. Int J Lab Hematol. 2018;40(Suppl 1):46-53. https://www.ncbi.nlm.nih.gov/pubmed/29741258. https://doi.org/10.1111/ijlh.12818.
30 Winkelman JW, Tanasijevic MJ, Zahniser DJ. A novel automated slide-based technology for visualization, counting, and characterization of the formed elements of blood: a proof of concept study. Arch Pathol & Lab Med. 2017;141(8):1107-12. https://www.ncbi.nlm.nih.gov/pubmed/28421831. https://doi.org/10.5858/arpa.2016-0633-OA.
Published
03/22/2020
How to Cite
Yturralde, E. M. ., Bulseco-Damian, K. ., & Geraldino, N. . (2020). Comparison of Digital Image Analysis and Conventional Microscopy in Evaluating Erythrocyte Morphology in Peripheral Blood Smears. PJP, 5(1), 10–18. https://doi.org/10.21141/PJP.2020.02
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