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Image Analysis

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Abstract

In the field of digital pathology, image analysis refers to the computer-aided diagnostic assessment of whole slide images (WSIs). While image analysis is clearly another application of WSI, we feel that the subject has become vast enough to warrant its own chapter. The potential of digital pathology has taken another giant step with the emergence of computer-assisted WSI analysis. To overcome challenges related to optimizing speed and accuracy, numerous statistical manipulations and algorithms have been generated adapted, and adopted to enhance the detection, quantification, and characterization of pathology. In this chapter, both the histories and current state of digital pathology and WSI analysis are reviewed, as well as the challenges that remain to optimize their use. It is clear that the potential of digital pathology is almost boundless, but that much work remains to be done.

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References

  1. Fanshawe, T.R., Lynch, A.G., Ellis, I.O., Green, A.R., Hanka, R.: Assessing agreement between multiple raters with missing rating information, applied to breast cancer tumour grading. PLoS ONE 3, e2925 (2008)

    Article  Google Scholar 

  2. Gurcan, M.N., Boucheron, L., Can, A., et al.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 141–171 (2009)

    Article  Google Scholar 

  3. Roychowdhury, A., Basu, S., Bandyapadhyay, A., Bhattacharya, P., Mitra, R.B.: Kappa statistics in the screening of malignancy of prostate. J. Indian Med. Assoc. 109, 786–789 (2011)

    Google Scholar 

  4. Core-Needle Biopsy for Breast Abnormalities: Clinician’s Guide. U.S. Department of Health and Human Services (2010)

    Google Scholar 

  5. Mendez, A.J., Tahoces, P.G., Lado, M.J., Souto, M., Vidal, J.J.: Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms. Med. Phys. 25, 957–964 (1998)

    Article  Google Scholar 

  6. Tang, J., Rangayyan, R.M., Xu, J., El Naqa, I., Yang, Y.: Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans. Inf. Technol. Biomed. 13, 236–251 (2009)

    Article  Google Scholar 

  7. Varga, V.S., Ficsor, L., Kamaras, V., et al.: Automated multichannel fluorescent whole slide imaging and its application for cytometry. Cytometry A. 75, 1020–1030 (2009)

    Article  Google Scholar 

  8. Martina, J.D., Simmons, C., Jukic, D.M.: High-definition hematoxylin and eosin staining in a transition to digital pathology. J. Pathol. Inform. 2, 45 (2011)

    Article  Google Scholar 

  9. Webster, J.D., Michalowski, A.M., Dwyer, J.E., et al.: Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis. J. Pathol. Inform. 3, 18 (2012)

    Article  Google Scholar 

  10. Bautista, P., Yagi, Y.: Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance. J. Biomed. Opt. 17 (2012)

    Google Scholar 

  11. Tani, S.: Color standardization system implementing estimation method for absorption spectra of dye. Anal. Cell. Pathol. 34, 180 (2013)

    Google Scholar 

  12. Yagi, Y.: Color standardization and optimization in whole slide imaging. Diagn. Pathol. 6, S15 (2011)

    Article  Google Scholar 

  13. Keller, B., Chen, W., Gavrielides, M.A.: Quantitative assessment and classification of tissue-based biomarker expression with color content analysis. Arch. Pathol. Lab. Med. 136, 539–550 (2012)

    Article  Google Scholar 

  14. Nederlof, M., Watanabe, S., Burnip, B., Taylor, D.L., Critchley-Thorne, R.: High-throughput profiling of tissue and tissue model microarrays: combined transmitted light and 3-color fluorescence digital pathology. J. Pathol. Inform. 2, 50 (2011)

    Article  Google Scholar 

  15. Hipp, J., Cheng, J., Pantanowitz, L., et al.: Image microarrays (IMA): digital pathology’s missing tool. J. Pathol. Inform. 2, 47 (2011)

    Article  Google Scholar 

  16. Feldman, M.D.: Beyond morphology: whole slide imaging, computer-aided detection, and other techniques. Arch. Pathol. Lab. Med. 132, 758–763 (2008)

    Google Scholar 

  17. Nanda, R.: Targeting the human epidermal growth factor receptor 2 (HER2) in the treatment of breast cancer: recent advances and future directions. Rev. Recent Clin. Trials 2, 111–116 (2007)

    Article  Google Scholar 

  18. Angell, H.K., Gray, N., Womack, C., et al.: Digital pattern recognition-based image analysis quantifies immune infiltrates in distinct tissue regions of colorectal cancer and identifies a metastatic phenotype. Br. J. Cancer 109, 1618–1624 (2013)

    Article  Google Scholar 

  19. Goode, A., Gilbert, G., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: A vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013)

    Article  Google Scholar 

  20. Open Slide: National Institutes of Health, Clinical and Translational Science Institute, University of Pittsburgh (2014)

    Google Scholar 

  21. Bioformats: Laboratory for Optical and Computational Instrumentation (2014)

    Google Scholar 

  22. Isaacs, M., Lennerz, J.K., Yates, S., et al.: Implementation of whole slide imaging in surgical pathology: a value added approach. J. Pathol. Inform. 2, 39 (2011)

    Article  Google Scholar 

  23. McClintock, D.S., Lee, R.E., Gilbertson, J.R.: Using computerized workflow simulations to assess the feasibility of Whole Slide Imaging full adoption in a high volume histology laboratory. Anal. Cell. Pathol. 34, 182–184 (2013)

    Google Scholar 

  24. Krupinski, E.A.: Optimizing the pathology workstation “cockpit”: challenges and solutions. J. Pathol. Inform. 1, 19 (2010)

    Article  Google Scholar 

  25. Amin, M., Sharma, G., Parwani, A.V., et al.: Integration of digital gross pathology images for enterprise-wide access. J. Pathol. Inform. 3, 10 (2012)

    Article  Google Scholar 

  26. Wang, F., Oh, T.W., Vergara-Niedermayr, C., Kurc, T., Saltz, J.: Managing and querying whole slide images. In: Proceedings of SPIE, vol. 8319(pii), pp. 83190J, 16 Feb 2012

    Google Scholar 

  27. Wang, Y., Williamson, K.E., Kelly, P.J., James, J.A., Hamilton, P.W.: SurfaceSlide: a multitouch digital pathology platform. PLoS ONE 7, e30783 (2012)

    Article  Google Scholar 

  28. Gurcan, M.N., Boucheron, L., Can, A., et al.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 141–171 (2009)

    Article  Google Scholar 

  29. Can, A., Bello, M., Cline, H.C., Tao, X., Ginty, F., Sood, A., Gerdes, M., Montalto, M.: Multimodal imaging of histological tissue sections. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 288–291 (2008)

    Google Scholar 

  30. Yang, L., Meer, P., Foran, D.J.: Unsupervised segmentation based on robust estimation and color active contour models. IEEE Trans. Inf. Technol. Biomed. 9, 475–486 (2005)

    Article  Google Scholar 

  31. Wang, Y.Y., Chang, S.C., Wu, L.W., Tsai, S.T., Sun, Y.N.: A color-based approach for automated segmentation in tumor tissue classification. Conf. Proc. IEEE Eng. Med. Biol. Soc. 6577–6580 (2007)

    Google Scholar 

  32. Sun, Y.N., Wang, Y.Y., Chang, S.C., Wu, L.W., Tsai, S.T.: Color-based tumor tissue segmentation for the automated estimation of oral cancer parameters. Microsc. Res. Tech. 73, 5–13 (2010)

    Google Scholar 

  33. Kayser, G., Kayser, K.: Quantitative pathology in virtual microscopy: history, applications, perspectives. Acta Histochem. 115, 527–532 (2013)

    Article  Google Scholar 

  34. Gurcan, M.N., Boucheron, L., Can, A., et al.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 141–171 (2009)

    Article  Google Scholar 

  35. Bello, M., Can, A., Tao, X.: Accurate registration and failure detection in tissue micro array images. In: 5th IEEE International Symposium Biomedical Imaging: From Nano to Macro, pp. 368–371 (2008)

    Google Scholar 

  36. Narasimha-Iyer, H., Can, A., Roysam, B., et al.: Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans. Biomed. Eng. 53, 1084–1098 (2006)

    Article  Google Scholar 

  37. Bibbo, M., Kim, D.H., Pfeifer, T., et al.: Histometric features for the grading of prostatic carcinoma. Anal. Quant. Cytol. Histol. 13, 61–68 (1991)

    Google Scholar 

  38. Belein, J.A., Baak, J.P., Van Diest, P.J., van Ginkel, A.H.: Counting mitoses by image processing in Feulgen stained breast cancer sections: the influence of resolution. Cytometry 28, 135–140 (1997)

    Article  Google Scholar 

  39. Markiewicz, T., Osowski, S., Patera, J., Kozlowski, W.: Image processing for accurate cell recognition and count on histologic slides. Anal. Quant. Cytol. Histol. 28, 281–291 (2006)

    Google Scholar 

  40. Kim, Y.L., Romeike, B.F., Uszkoreit, J., Feiden, W.: Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas. Clin. Neuropathol. 25, 67–73 (2006)

    Google Scholar 

  41. Sont, J.K., De Boer, W.I., van Schadewijk, W.A., et al.: Fully automated assessment of inflammatory cell counts and cytokine expression in bronchial tissue. Am. J. Respir. Crit. Care Med. 167, 1503 (2003)

    Article  Google Scholar 

  42. Brock, R., Hink, M.A., Jovin, T.M.: Fluorescence correlation microscopy of cells in the presence of autofluorescence. Biophys. J. 75, 2547–2557 (2014)

    Article  Google Scholar 

  43. Gerencser, A.A., Adam-Vizi, V.: Selective, high-resolution fluorescence imaging of mitochondrial Ca2+ concentration. Cell Calcium 30, 311–321 (2001)

    Article  Google Scholar 

  44. Can, A., Bello, M., Cline, H.E., Tao, X., Ginty, F., Sood, A., Gerdes, M., Montalto, M.: Multimodal imaging of histological tissue sections. In: 5th IEEE International Symposium Biomedical Imaging: From Nano to Macro 2008, pp. 288–291 (2008)

    Google Scholar 

  45. Sharangpani, G.M., Joshi, A.S., Porter, K., et al.: Semi-automated imaging system to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer. J. Microsc. 226, 244–255 (2007)

    Article  MathSciNet  Google Scholar 

  46. Gundersen, H.J., Osterby, R.: Optimizing sampling efficiency of stereological studies in biology: or ‘do more less well!’. J. Microsc. 121, 65–73 (1981)

    Article  Google Scholar 

  47. Bilgin, C.C., Bullough, P., Plopper, G.E., Yener, B.: ECM-Aware Cell-Graph mining for bone tissue modeling and classification. Data Min. Knowl. Discov. 20, 416–438 (2009)

    Article  MathSciNet  Google Scholar 

  48. Doyle, S., Hwang, M., Shah, K., et al.: Automated grading of prostate cancer using architectural and textural image features. IEEE Explore 1284–1287 (2007)

    Google Scholar 

  49. Sertel, O., Kong, J., Shimada, H., et al.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit. 42, 1093–1103 (2009)

    Article  Google Scholar 

  50. Sertel, O., Kong, J., Shimada, H., et al.: Computer-aided prognosis of neuroblastoma on whole-slide images: classifying grade of neuroblastic differentiation. Pattern Recognit. 42, 1080–1192 (2009)

    Article  Google Scholar 

  51. Doyle, S., Madabhushi, A., Feldman, M., Tomaszeweski, J.: A boosting cascade for automated detection of prostate cancer from digitized histology. Med. Image Comput. Comput. Assist. Interv. 9, 504–511 (2006)

    Google Scholar 

  52. Pudil, P., Novovivcova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15, 1119–1125 (1994)

    Article  Google Scholar 

  53. Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19, 153–158 (1997)

    Article  Google Scholar 

  54. Freund, Y., Shapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comp. Syst. Sci. 55, 119–139 (1997)

    Article  MATH  Google Scholar 

  55. Perkins, S., Lacker, K., Theiler, J.: Fast, incremental feature selection by gradient descent in function space. J. Mach. Learn. Res. 3, 1333–1356 (2003)

    MATH  MathSciNet  Google Scholar 

  56. Qureshi, H., Sertel, O., Rajpoot, N., Wilson, R., Gurcan, M.N.: Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008, pp. 196–204

    Google Scholar 

  57. Pudil, P., Novovivcova, J.: Novel methods for feature subset selection with respect to problem knowledge. In: Feature Extraction, Construction and Selection, p. 101 (1998)

    Google Scholar 

  58. Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, Los Altos (2006)

    Google Scholar 

  59. Ding, C., He, X., Zha, H., Simon, H.D.: Adaptive dimension reduction for clustering high dimensional data. In: International Conference on Data Mining (2002)

    Google Scholar 

  60. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: A survey of multilinear subspace learning for tensor data. IEEE Rev. Biomed. Eng. 2, 171 (2009)

    Google Scholar 

  61. Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, Berlin (2002)

    Google Scholar 

  62. Martinez, A., Kak, A.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23, 228–233 (2001)

    Article  Google Scholar 

  63. Chawla, N.V., Bowyer, K.W.: Designing Multiple Classifier Systems for Face Recognition. Department of Computer Science and Engineering, University of Notre Dame (2014)

    Google Scholar 

  64. Hu, H., Zahorian, S.A.: Dimensionality Reduction Methods for HMM Phonetic Recognition. Department of Electrical and Computer Engineering, Binghamton University (2010)

    Google Scholar 

  65. Shaw, B., Jebara, T.: Structure preserving embedding. In: Proceedings of the 26th Annual International Conference on Machine Learning—ICML’09. 1, 2009

    Google Scholar 

  66. Bingham, E., Mannila, H.: Random projection in dimensionality reduction. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’01. 245, 2001

    Google Scholar 

  67. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  68. Gao, X., Wang, X., Tao, D., Li, X.: Supervised Gaussian process latent variable model for dimensionality reduction. IEEE Trans. Syst. Man Cybern. B Cybern. 41, 425–434 (2011)

    Article  Google Scholar 

  69. Madabhushi, A., Doyle, S., Lee, J.H., et al.: Integrated diagnostics: a conceptual framework with examples. Clin. Chem. Lab. Med. 48, 998 (2010)

    Article  Google Scholar 

  70. Doyle, S., Hwang, M., Shah, K., Madabhushi, A., Feldman, M., Tomaszeweski, J.: Automated grading of prostate cancer using architectural and textural image features. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007, pp. 1284–1287

    Google Scholar 

  71. Rajpoot, N., Mohammad, A., Bhalerao, A.: Unsupervised learning of shape manifolds. In: Proceedings of the British Machine Vision Conference 2007 (2014)

    Google Scholar 

  72. Coifman, R., Lafon, S., Lee, A., Maggioni, M., Nadler, B., Warner, F., Zucker, S.: Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. In: Proceedings of the National Academy of Sciences, pp. 7426–7431 (2005)

    Google Scholar 

  73. Chawla, N.V., Bowyer, K.W.: Designing Multiple Classifier Systems for Face Recognition. Department of Computer Science and Engineering, University of Notre Dame (2014)

    Google Scholar 

  74. Doyle, S., Rodriguez, C., Madabhushi, A., Tomaszeweski, J., Feldman, M.: Detecting prostatic adenocarcinoma from digitized histology using a multi-scale, hierarchical classification approach. In: IEEE Engineering in Medicine and Biology Conference, pp. 4759–4762 (2014)

    Google Scholar 

  75. Jafari-Khouzani, K., Soltanian-Zadeh, H.: Multiwavelet grading of pathological images of prostate. IEEE Trans. Biomed. Eng. 50, 697–704 (2003)

    Article  Google Scholar 

  76. Tabesh, A., Teverovskiy, M., Pang, H.Y., et al.: Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 26, 1366–1378 (2007)

    Article  Google Scholar 

  77. Rajpoot, K., Rajpoot, N.: Optimization for hyperspectral colon tissue cell classification. In: Medical Image Computing and Computer-Assisted Intervention. MICCAI-2004, pp. 829–837 (2004)

    Google Scholar 

  78. Esgiar, A.N., Naguib, R.N., Sharif, B.S., Bennett, M.K., Murray, A.: Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans. Inf. Technol. Biomed. 2, 197–203 (1998)

    Article  Google Scholar 

  79. Qureshi, H., Sertel, O., Rajpoot, N., Wilson, R., Gurcan, M.N.: Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification. Med. Image Comput. Comput. Assist. Interv. 11, 196–204 (2008)

    Google Scholar 

  80. van de Wouwer, G., Weyn, B., Scheunders, P., et al.: Wavelets as chromatin texture descriptors for the automated identification of neoplastic nuclei. J. Microsc. 197, 25–35 (2000)

    Article  Google Scholar 

  81. Winzer, K.J., Bellach, J., Hufnagl, P.: Long-term analysis to objectify the tumour grading by means of automated microscopic image analysis of the nucleolar organizer regions (AgNORs) in the case of breast carcinoma. Diagn. Pathol. 8, 56 (2013). doi: 10.1186/1746-1596-8-56

  82. Weyn, B., van de Wouwer, G., van Daele, A., et al.: Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. Cytometry 33, 32–40 (1998)

    Article  Google Scholar 

  83. Garcia Rojo, M.: State of the art and trends for digital pathology. Stud. Health Technol. Inform. 179, 15–28 (2012)

    Google Scholar 

  84. Evans, A., Sinard, J.H., Fatheree, L.A., Henricks, W.H., Carter, A.B., Contis, L., et al.: Validating whole slide imaging for diagnostic purposes in pathology: recommendations of the College of American Pathologists (CAP) pathology and laboratory quality centre. Anal. Cell. Pathol. 34, 174 (2011)

    Google Scholar 

  85. Singh, R., Chubb, L., Pantanowitz, L., Parwani, A.: Standardization in digital pathology: Supplement 145 of the DICOM standards. J. Pathol. Inform. 2, 23 (2011)

    Article  Google Scholar 

  86. Yagi, Y., Rojo, M.G., Kayser, K., et al.: The first congress of the International Academy of Digital Pathology: digital pathology comes of age. Anal. Cell. Pathol. (AMST) 35, 1–2 (2012)

    Article  Google Scholar 

  87. Huisman, A.: Digital pathology for education. Stud. Health Technol. Inform. 179, 68–71 (2012)

    Google Scholar 

  88. Wilbur, D.C.: Digital cytology: current state of the art and prospects for the future. Acta Cytol. 55, 227–238 (2011)

    Article  Google Scholar 

  89. Tsuchihasi, Y.: Expanding application of digital pathology in Japan—from education, telepathology to autodiagnosis. Diagn. Pathol. 6, S19 (2011)

    Article  Google Scholar 

  90. Hamilton, P.W., Wang, Y., McCullough, S.J.: Virtual microscopy and digital pathology in training and education. APMIS 120, 305–315 (2012)

    Article  Google Scholar 

  91. Schwartz, J.: Expanding the lab’s reach with digital pathology. MLO Med. Lab. Obs. 43, 41 (2011)

    Google Scholar 

  92. Glotsos, D., Tohka, J., Ravazoula, P., Cavouras, D., Nikifordis, G.: Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines. Int. J. Neural Syst. 15, 1–11 (2005)

    Article  Google Scholar 

  93. Glotsos, D., Kalatzis, I., Spyridonos, P., et al.: Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme. Comput. Methods Programs Biomed. 90, 251–261 (2008)

    Article  Google Scholar 

  94. Ho, J., Parwani, A., Jukic, D.M., et al.: Use of whole slide imaging in surgical pathology quality assurance: design and pilot validation studies. Hum. Pathol. 37, 322–331 (2006)

    Article  Google Scholar 

  95. Kalinski, T., Zwonitzer, R., Sel, S., et al.: Virtual 3D microscopy using multiplane whole slide images in diagnostic pathology. Am. J. Clin. Pathol. 130, 259–264 (2008)

    Article  Google Scholar 

  96. Gilbertson, J.R., Ho, J., Anthony, L., et al.: Primary histologic diagnosis using automated whole slide imaging: a validation study. BMC Clin. Pathol. 27, 4 (2006)

    Article  Google Scholar 

  97. Fine, J.L., Grzybicki, D.M., Silowash, R., et al.: Evaluation of whole slide image immunohistochemistry interpretation in challenging prostate needle biopsies. Hum. Pathol. 39, 564–572 (2008)

    Article  Google Scholar 

  98. Nassar, A., Cohen, C., Agersborg, S.S., et al.: A multisite performance study comparing the reading of immunohistochemical slides on a computer monitor with conventional manual microscopy for estrogen and progesterone receptor analysis. Am. J. Clin. Pathol. 135, 461–467 (2011)

    Article  Google Scholar 

  99. Pantanowitz, L.: Digital images and the future of digital pathology. J. Pathol. Inform. 10, 1 (2010)

    Article  Google Scholar 

  100. Pantanowicz, L., Szymas, J., Yagi, Y., Wilbur, D.: Whole slide imaging for educational purposes. J. Pathol. Inform. 3 (2012)

    Google Scholar 

  101. Al-Janabi, S., Huisman, A., Van Diest, P.J.: Digital pathology: current status and future perspectives. Histopathology 61, 1–9 (2012)

    Article  Google Scholar 

  102. Hedvat, C.V.: Digital microscopy: past, present, and future. Arch. Pathol. Lab. Med. 134, 1666–1670 (2010)

    Google Scholar 

  103. Pantanowitz, L., Wiley, C.A., Demetris, A., et al.: Experience with multimodality telepathology at the University of Pittsburgh Medical Center. J. Pathol. Inform. 3, 45 (2013). doi: 10.4103/2153-3539.104907. Epub 20 Dec 2012

  104. Dennis, T., Start, R.D., Cross, S.S.: The use of digital imaging, video conferencing, and telepathology in histopathology: a national survey. J. Clin. Pathol. 58, 254–258 (2005)

    Article  Google Scholar 

  105. Johnson, D.E.: NightHawk teleradiology services: a template for pathology? Arch. Pathol. Lab. Med. 132, 745–747 (2008)

    Google Scholar 

  106. Cornish, T.C., Swapp, R.E., Kaplan, K.J.: Whole-slide imaging: routine pathologic diagnosis. Adv. Anat. Pathol. 19, 152–159 (2012)

    Article  Google Scholar 

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Sucaet, Y., Waelput, W. (2014). Image Analysis. In: Digital Pathology. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-08780-1_4

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