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Morphometric Image Analysis and its Applications in Biomedicine Using Different Microscopy Modes

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Microscopy Techniques for Biomedical Education and Healthcare Practice

Part of the book series: Biomedical Visualization ((BV,volume 2))

Abstract

In biomedicine, morphometric image analysis is defined as the merging of geometry and histology. Morphometry refers to a quantitative analysis of the size and shape of geometric features of cells, cell organelles, and/or biomarkers. Modern morphometry utilizes advanced computer-assisted image analysis software to interface an image with geometric software that objectively measures specific histological characteristics. It may be accomplished on the whole image, or on a particular area of interest after image selection (segmentation).

Morphometric image analysis is widely used in biomedical studies and pathology. Its applications include differentiating between benign and malignant tissues based on the nuclear morphology of the cells, as well as quantitation of immunohistochemical or immunofluorescent assays for the expression of specific biomarkers in normal and/or pathological conditions. Accurate calibration of the microscope using standards and controls is crucial for precise and reproducible quantitation. Morphometric image analysis may be performed on plastic or paraffin-embedded and specifically stained tissue sections by use of conventional light, e.g., fluorescence microscopy or on thick specimens by means of confocal laser scanning microscopy, as well as at a subcellular level using transmission electron microscopy.

This review chapter defines, illustrates, and encapsulates the importance of morphometric image analysis in biomedical research using different microscopy modes. It also covers the methodology and problems related to quantitation using immunohistochemical, immunofluorescent, and immunoelectron images.

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References

  • Abdalla F, Boder J, Markus R, Hashmi H, Buhmeida A, Collan Y (2009) Correlation of nuclear morphometry of breast cancer in histological sections with clinicopathological features and prognosis. Anticancer Res 29:1771–1776

    PubMed  Google Scholar 

  • Abdollahzadeh A, Belevich I, Jokitalo E, Tohka J, Sierra A (2019) Automated 3D axonal morphometry of white matter. Sci Rep 9(1):6084

    Article  PubMed  PubMed Central  Google Scholar 

  • Al-amri SS, Kalyankar NV, Khamitkar SD (2010) Image segmentation by using threshold techniques. J Comput 2(5):83–86

    Google Scholar 

  • Ambellan F, Tack A, Ehlkeba M, Zachow S (2019) Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the Osteoarthritis initiative. Med Image Anal 52:109–118

    Article  PubMed  Google Scholar 

  • Araña RVE (2015) Geometric morphometric analysis of individual variation in bumblebee wings. IAMURE Int J Ecol Conserv 11:1–15

    Google Scholar 

  • Araújo SJ, Llimargas M (2023) Time-lapse imaging and morphometric analysis of tracheal development in drosophila. In: Margadant C (ed) Cell migration in three dimensions. Methods in molecular biology, vol 2608. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2887-4_11

    Chapter  Google Scholar 

  • Balafar MA, Ramli AR, Saripan MI, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33:261–274

    Article  Google Scholar 

  • Bhattacharya P, Edwards K, Schmid KL (2022) Segmentation methods and morphometry of confocal microscopy imaged corneal epithelial cells. Cont Lens Anterior Eye 45(6):101720

    Article  PubMed  Google Scholar 

  • Bostwick DG, Cheng L (2020) Neoplasms of the Prostate. In: Cheng L, Maclennan GT & Bostwick DG (eds) Urologic Surgical Pathology (Fourth Edition), chapter 9, pp. 415–525, Elsevier. ISBN 978-0323549417, https://doi.org/10.1016/B978-0-323-54941-7.00009-8

  • Buhmeida A (2006) Quantitative pathology: historical background, clinical research and application of nuclear morphometry and DNA image cytometry. Libyan J Med 1:126–139

    Article  PubMed  PubMed Central  Google Scholar 

  • Burke RT, Orth JD (2016) Through the looking glass: time-lapse microscopy and longitudinal tracking of single cells to study anti-cancer therapeutics. J Vis Exp 111:e53994

    Google Scholar 

  • Chang L, Shukla DK (2018) Imaging studies of the HIV-infected brain. In: Handbook of Clinical Neurology, Vol. 152 (3rd series), Brew BJ, Ed. The Neurology of HIV Infection, Chapter 18, pp 229–264, Elsevier. https://doi.org/10.1016/B978-0-444-63849-6.00018-9

  • Chvátal A, Anderová M, Kirchhoff F (2007) Three-dimensional confocal morphometry—a new approach for studying dynamic changes in cell morphology in brain slices. J Anat 210:671–683

    Article  PubMed  PubMed Central  Google Scholar 

  • Collins JL, van Knippenberg B, Ding K, Kofman AV (2019) Time-lapse microscopy. Cell Culture. https://doi.org/10.5772/intechopen.81199

  • Curry DP, Dias FJ, Sosthenes MC, Dos Santos Haemmerle CA, Ogawa K, Da Silva MC, Mardegan Issa JP, Iyomasa MM, Watanabe IS (2013) Morphometric, quantitative, and three-dimensional analysis of the heart muscle fibers of old rats: transmission electron microscopy and high-resolution scanning electron microscopy methods. Microsc Res Tech 76(2):184–195

    Article  Google Scholar 

  • Daimary D, Bora MB, Amitab K, Kandar D (2020) Brain tumor segmentation from MRI images using hybrid convolutional neural networks. Procedia Comput Sci 167:2419–2428

    Article  Google Scholar 

  • De Paul AL, Mukdsi JH, Petiti JP, Gutiérrez S, Quintar AA, Cristina A. Maldonado CA, Torres AI (2012). Immunoelectron microscopy: a reliable tool for the analysis of cellular processes, applications of immunocytochemistry, Dr. Hesam Dehghani (Ed.). InTech

    Google Scholar 

  • Delahunt B, Sika-Paotonu D, Bethwaite PB, William Jordan T, Magi-Galluzzi C, Zhou M, Samaratunga H, Srigley JR (2011) Grading of clear cell renal cell carcinoma should be based on nucleolar prominence. Am J Surg Pathol 35(8):1134–1139

    Article  PubMed  Google Scholar 

  • Denk W, Horstmann H (2004) Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol 2(11):e329

    Article  PubMed  PubMed Central  Google Scholar 

  • Dujardin J-P (2011) Modern Morphometrics of Medically Important Insects. In: Tibayrenc M (ed) Genetics and Evolution of Infectious Diseases. chapter 16, pp. 473–501. Elsevier. ISBN 9780123848901. https://doi.org/10.1016/B978-0-12-384890-1.00016-9

  • Edgar JM, Smith RS, Duncan ID (2020) Transmission electron microscopy and morphometry of the CNS white matter. In: Babetto E (ed) Axon degeneration. Methods in molecular biology, vol 2143. Humana, New York, NY

    Google Scholar 

  • Filoni A, Alaibac M (2019) Reflectance confocal microscopy in evaluating skin cancer: a Clinicians's perspective. Front Oncol 9:1457

    Article  PubMed  PubMed Central  Google Scholar 

  • Franchi A, Gallo O, Massi D, Baroni G, Santucci M (2004) Tumor lymphangiogenesis in head and neck squamous cell carcinoma: a morphometric study with clinical correlations. Cancer 101(5):973–978

    Article  PubMed  Google Scholar 

  • Gil J, Wu H, Wang BY (2002) Image analysis and morphometry in the diagnosis of breast cancer. Microsc Res Tech 59(2):109–118

    Article  PubMed  Google Scholar 

  • Gill M, Alessi-Fox C, Kose K (2019) Artifacts and landmarks: pearls and pitfalls for in vivo reflectance confocal microscopy of the skin using the tissue-coupled device. Dermatol Online J 25(8):1–13

    Article  Google Scholar 

  • Gonzalez R, Woods R (2002) Digital image processing, 2nd edn. Prentice-Hall, Upper Saddle River, NJ, USA

    Google Scholar 

  • Goto M, Abe O, Hagiwara A, Fujita S, Kamagata K, Hori M, Aoki S, Osada T, Konishi S, Masutani Y, Sakamoto H, Sakano Y, Kyogoku S, Daida H (2022) Advantages of using both voxel- and surface-based morphometry in cortical morphology analysis: a review of various applications. Magn Reson Med Sci 21(1):41–57

    Article  PubMed  PubMed Central  Google Scholar 

  • Griffiths G, Sloth J (1992). Cryoultramicrotomy and immunolabelling workshop course notes. Electron Microscopy Unit, University of Natal, South Africa

    Google Scholar 

  • Grosche J, Kettenmann H, Reichenbach A (2002) Bergmann glial cells form distinct morphological structures to interact with cerebellar neurons. J Neurosci Res 68:138–149

    Article  CAS  PubMed  Google Scholar 

  • Gulati NM, Torian U, Gallagher JR, Harris AK (2019) Immunoelectron microscopy of viral antigens. Curr Protoc Microbiol 53(1):e86

    Article  PubMed  PubMed Central  Google Scholar 

  • Hallgrimsson B, Percival CJ, Green R, Young NM, Mio W, Marcucio R (2015) Morphometrics, 3D imaging, and craniofacial development. Curr Top Dev Biol 115:561–597

    Article  PubMed  PubMed Central  Google Scholar 

  • Hirsch FR, McElhinny A, Stanforth D et al (2017) PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J Thorac Oncol 12:208–222

    Article  PubMed  Google Scholar 

  • Jasim S, Dean DS, Gharib H (2015) Fine-needle aspiration biopsy of the thyroid gland. In: Feingold KR et al (eds) Endotext [internet]. MDText.com, Inc., South Dartmouth (MA), p 2000

    Google Scholar 

  • Jonuscheit IS, Doughty MJ, Ramaesh K (2011) In vivo confocal microscopy of the corneal endothelium: comparison of three morphometry methods after corneal transplantation. Eye (Lond) 25(9):1130–1137

    Article  CAS  PubMed  Google Scholar 

  • Joubert, B C. (2010). The interaction of lymphogranuloma venereum and oculogenital chlamydia trachomatis with human keratinocytes and cervical epithelium. PhD thesis, UKZN, South Africa

    Google Scholar 

  • Kashyap A, Jain M, Shukla S, Andley M (2018) Role of nuclear morphometry in breast cancer and its correlation with cytomorphological grading of breast cancer: a study of 64 cases. J Cytol 35(1):41–45

    Article  PubMed  PubMed Central  Google Scholar 

  • Laishram S (2017) Nuclear morphometric application in the quantitative description of breast lesions. J Med Res 3(5):255–257

    Article  Google Scholar 

  • Lauwers F, Cassot F, Lauwers-Cances V, Puwanarajah P, Duvernoy H (2008) Morphometry of the human cerebral cortex microcirculation: general characteristics and space-related profiles. NeuroImage 39(3):936–948

    Article  PubMed  Google Scholar 

  • Lawrence D, Alvis R, Olson D (2008) Specimen preparation for cross-section atom probe analysis. Microsc Microanal 14(S2):1004–1005

    Article  Google Scholar 

  • Lestrel PE (2000) Morphometrics for the life sciences. World Scientific Publishing Company

    Book  Google Scholar 

  • Levitt JJ, Bobrow L, Lucia D, Srinivasan P (2010) A selective review of volumetric and morphometric imaging in schizophrenia. Curr Top Behav Neurosci 4:243–281

    Article  PubMed  Google Scholar 

  • Lloreta-Trull J, Bielsa-Galí O, Domínguez-Solà D, Arumí-Uría M, Pavesi M, Gelabert A, Serrano-Figueras S (2001) Ultrastructural morphometry of nucleoli: potential usefulness for objective grading of clear cell renal cell carcinoma. Ultrastruct Pathol 25(2):105–110

    Article  CAS  PubMed  Google Scholar 

  • Maduray K, Moodley J, Naicker T (2016) Morphometrical analysis of placental functional efficiency in normotensive versus pre-eclamptic south African black women. Hypertens Pregnancy 35(3):361–370

    Article  CAS  PubMed  Google Scholar 

  • Matos LL, Trufelli DC, de Matos MG, da Silva Pinhal MA (2010) Immunohistochemistry as an important tool in biomarkers detection and clinical practice. Biomark Insights 5:9–20

    Article  PubMed  PubMed Central  Google Scholar 

  • Merhar V, Onyangunga O, Moodley J, Naicker T (2022) Comparative morphometric image analysis of LYVE-1 and Podoplanin in HIV infected preeclamptic women. In: Sotirov SS, Pencheva T, Kacprzyk J, Atanassov KT, Sotirova E, Staneva G (eds) Contemporary methods in bioinformatics and biomedicine and their applications. Springer publishing, pp 400–408

    Chapter  Google Scholar 

  • Mitteröcker P (2021) Morphometrics in evolutionary developmental biology. In: Nuño de la Rosa L, Müller GB (eds) Evolutionary developmental biology. Springer, Cham

    Chapter  Google Scholar 

  • Mitteroecker P, Gunz P (2009) Advances in geometric morphometrics. Evol Biol 36:235–247

    Article  Google Scholar 

  • Mudaliar K, Hutchens K (2013) Morphometric image analysis as a tool in the diagnosis of transected squamous neoplasms. J Clin Anat Pathol 1:1–5

    Google Scholar 

  • Naicker T (2002). Immunolocalisation of transforming growth factor beta and its latent peptide in the placental bed of normal and hypertensive pregnancy. PhD thesis UKZN, South Africa

    Google Scholar 

  • Naicker T, Dorsamy E, Ramsuran D, Burton GJ, Moodley J (2013) The role of apoptosis versus proliferation on trophoblast cell invasion in the placental bed of normotensive and hypertensive pregnancies. Hypertens Pregnancy 32(3):245–256

    Article  CAS  PubMed  Google Scholar 

  • Naicker T (2023) Personal communication. University of KwaZulu-Natal, Optics & Imaging Centre

    Google Scholar 

  • Ny A, Vandevelde W, Hohensinner P, Beerens M, Geudens I, Diez-Juan A, Brepoels K, Plaisance S, Krieg PA, Langenberg T, Vinckier S, Luttun A, Carmeliet P, Dewerchin M (2013) A transgenic Xenopus laevis reporter model to study lymphangiogenesis. Biol Open 2(9):882–890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Onyangunga O, Moodley J, Odun-Ayo F, Naicker T (2018) Immunohistochemical localiza-tion of podoplanin in the placentas of HIV-positive preeclamptic women. Turk J Med Sci 48:916–924

    Article  CAS  PubMed  Google Scholar 

  • Onyangunga OA, Moodley J, Merhar V, Ofusori A, Naicker T (2016) Lymphatic vascular endothelial hyaluronan receptor-1 immunoexpression in placenta of HIV infected pre-eclamptic women. J Reprod Immunol 117:81–88

    Article  CAS  PubMed  Google Scholar 

  • Pandian DJN, Ramdas A, Ambroise MM (2021) Image analysis-assisted nuclear morphometric study of benign and malignant breast aspirates. J Microsc Ultrastruct 9(3):114–118

    Article  Google Scholar 

  • Papathomas TG, Pucci E, Giordano TJ et al (2016) An international Ki67 reproducibility study in adrenal cortical carcinoma. Am J Surg Pathol 40:569–576

    Article  PubMed  Google Scholar 

  • Papaxoinis K, Patsouris E, Athanassiadou P, Nicolopoulou-Stamati P (2009) Contribution of nuclear morphometry by confocal laser scanning microscopy to the diagnosis of malignant bile duct strictures. Acta Cytol 53(2):137–143

    Article  PubMed  Google Scholar 

  • Phillips T, Millett MM, Zhang X et al (2018) Development of a diagnostic programmed cell death 1-ligand 1 immunohistochemistry assay for nivolumab therapy in melanoma. Appl Immunohistochem Mol Morphol 26:6–12

    Article  CAS  PubMed  Google Scholar 

  • Rexhepaj E, Brennan DJ, Holloway P et al (2008) Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer. Breast Cancer Res 10:1–10

    Article  Google Scholar 

  • Rohlf FJ, Marcus LF (1993) A revolution in morphometrics. Trends Ecol Evol 8(4):129–132

    Article  Google Scholar 

  • Saood A, Hatem I (2021) COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 21(1):1–10

    Article  Google Scholar 

  • Sendaydiego JP, Torres MAJ, Demayo CG (2013) Describing wing geometry of Aedes aegypti using landmark-based geometric morphometrics. Int J Biosci Biochem Bioinforma 3(4):379–381

    Google Scholar 

  • Tosi P, Luzi P, Baak JP, Miracco C, Santopietro R et al (1986) Nuclear morphometry as an important prognostic factor in stage I renal cell carcinoma. Cancer 58:2512–2518

    Article  CAS  PubMed  Google Scholar 

  • Varaden D, Moodley J, Onyangunga OA, Thajasvarie Naicker T (2019) Morphometric image analysis of placental C-type lectin domain family2, member D (CLEC2D) immuno-expression in HIV associatedpre-eclampsia. Eur J Obstet Gynecol Reprod Biol: X 3:100039

    Article  CAS  PubMed  Google Scholar 

  • Wang SL, Wu MT, Yang SF, Chan HM, Chai CY (2005) Computerized nuclear morphometry in thyroid follicular neoplasms. Pathol Int 55:703–706

    Article  PubMed  Google Scholar 

  • Webster M, Sheets H (2010) A practical introduction to landmark-based geometric morphometrics. Paleontological Society Papers 16:163–188. https://doi.org/10.1017/S1089332600001868

    Article  Google Scholar 

  • Wen C, Huang M, Wang S, Su Y, Yang S, Chai C (2009) Diagnostic value of computerized nuclear morphometry for the prediction of malignancy in liver fine needle aspiration cytology. Acta Cytol 53:77–82

    Article  PubMed  Google Scholar 

  • Yang Q, Mori I, Sakurai T, Yoshimura G, Suzuma T, Nakamura Y, Nakamura M, Taniguchi E, Tamaki T, Umemura T, Kakudo K (2001) Correlation between nuclear grade and biological prognostic variables in invasive breast cancer. Breast Cancer 8(2):105–110

    Article  CAS  PubMed  Google Scholar 

  • Young JW, Locke JC, Altinok A, Rosenfeld N, Bacarian T, Swain PS et al (2011) Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy. Nat Protoc 7(1):80–88

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Biology, Medical Genetics and Microbiology, Faculty of Medicine, University Prof. Dr. Assen Zlatarov, Burgas, Bulgaria

    Vesselina Merhar

  2. Optics and Imaging Centre, Doris Duke Medical Research Institute, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa

    Thajasvarie Naicker

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  1. Vesselina Merhar
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  2. Thajasvarie Naicker
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Correspondence to Vesselina Merhar .

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  1. Department of Human Biology, University of Cape Town, Cape Town, South Africa

    Leonard Shapiro

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Merhar, V., Naicker, T. (2023). Morphometric Image Analysis and its Applications in Biomedicine Using Different Microscopy Modes. In: Shapiro, L. (eds) Microscopy Techniques for Biomedical Education and Healthcare Practice . Biomedical Visualization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-031-36850-9_2

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