MIAP – Web-based platform for the computer analysis of microscopic images to support the pathological diagnosis

• Autorzy: Markiewicz T. , Korzynska A. , Kowalski A. , Swiderska-Chadaj Z. , Murawski P. , Grala B. , Lorent M. , Wdowiak M. , Zak J. , Roszkowiak L. , Kozlowski W. , Pijanowska D.

Identyfikatory

DOI

10.1016/j.bbe.2016.06.006

• Języki publikacji: EN

Abstrakty

EN

The aim of the project is to design and to implement a web-based platform for the computer analysis of microscopic images which support the pathological diagnosis. The use of the platform will be free of charge. It offers: quantitative analysis of staining tissue sample' images, archiving microscopic images, peer consultation, and join work independently from distance between scientific collaborating centers to registered doctors, scientists and students. The use of proposed platform allows: (i) to save pathologists' time spend on quantitative analysis, (ii) to reduce consulting costs by replacing sending of the physical preparations by placing their images (mostly virtual slide) on the platform server, (iii) to increase reproducibility, comparability and objectivity of quantitative evaluations. These effects have a direct impact on improving the effectiveness and decreasing the costs of patients' treatment. This paper presents the main ideas of the project which deliver web-based system working as multi-functional, integrated, modular and scalable computer system. The details of hardware solutions, concept of the workflow in the platform, the programming language and interpreters, the specific tools and algorithms, and the user interfaces are described below. The practical solutions for web-based services in the area of medical image analysis, storage and retrieval are also presented and discussed.

Słowa kluczowe

EN

image processing; image analysis; digital pathology; virtual microscopy; whole slide imaging; immunohistochemistry; web platform

PL

przetwarzanie obrazu; analiza obrazu; patologia cyfrowa; mikroskopia wirtualna; immunohistochemia; platforma internetowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 4
• Strony: 597--609
• Opis fizyczny: Bibliogr. 56 poz., rys., tab.

Twórcy

autor

Markiewicz T.

• Military Institute of Medicine, Warsaw, Poland; Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Korzynska A.

• Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland

autor

Kowalski A.

• Military Institute of Medicine, Warsaw, Poland

autor

Swiderska-Chadaj Z.

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Murawski P.

• Military Institute of Medicine, Warsaw, Poland

autor

Grala B.

• Military Institute of Medicine, Warsaw, Poland

autor

Lorent M.

• Military Institute of Medicine, Warsaw, Poland

autor

Wdowiak M.

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland

autor

Zak J.

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland; Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland

autor

Roszkowiak L.

• Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland

autor

Kozlowski W.

• Military Institute of Medicine, Warsaw, Poland

autor

Pijanowska D.

• Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland

Bibliografia

• [1] Bueno G, Milagro Fernández-Carrobles M, Deniz O, García-Rojo M. New trends of emerging technologies in digital pathology. Pathobiology 2016;83:61–9.
• [2] Gracia Rojo M, Bueno G, Słodkowska J. Review of imaging solutions for integrated quantitative immunohistochemistry in the pathology daily practice. Folia Histochem Cytobiol 2009;47(3):349–54.
• [3] Xinga F, Yanga L. Robust nuclei/cell detection and segmentation for digital pathology and microscopic images: a comprehensive review. IEEE Rev Biomed Eng 2016;PP(99):1.
• [4] Jisha J, Nair MS, Kumar PRA, Wilscy M. A novel approach for detection and delineation of cell nuclei using feature similarity index measure. Biocybern Biomed Eng 2015;36 (1):76–88.
• [5] Garcia-Rojo M, Blobel B, Laurinavicius A. Studies in health technology and informatics, vol. 179. IOS Press; 2012.
• [6] Kayser G, Radziszowski D, Bzdyl P, Sommer R, Kayser K. Theory and implementation of an electronic, automated measurement system for images obtained from immunohistochemically stained slides. Anal Quant Cytol Histol 2006;28:27–38.
• [7] Kayser G, Radziszowski D, Bzdyl P, Werner M, Kayser K. Eamus – internet based platform for automated quantitative measurements in immunohistochemistry. Proc. International Society for Cellular Oncology (ISCO); 2005.
• [8] Intersimone D, Snoj V, Riosa F, Bortolotti N, Sverko S, Beltrami CA, et al. Transnational telepathology consultations using a basic digital microscope: experience in the Italy-Slovenjia INTERREG project ‘‘Patient without borders’’. BMC Diagn Pathol 2011;6(1):25.
• [9] Yagi Y, Kayser K. Whole slide images – application in education, research and routine. Proc. 24th Congress of Pathology; 2012.
• [10] Kayser K, Borkenfeld S, Kayser G. Virtual slides in open access publication and medical forum. Proc. 24th Congress of Pathology; 2012.
• [11] Foran DJ, Yang L, Chen W, Hu J, Goodell LA, Reiss M, et al. ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. J Am Med Inform Assoc 2011;18(4):403–15.
• [12] Lezoray O, Elmoataz A, Cardot H, Gougeon G, Lecluse M, Elie H, et al. Segmentation of color images from serous cytology for automated cell classification. Anal Quant Cytol Histopathol 2000;22:311–22.
• [13] Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histopathol 2001;23(4):291–9.
• [14] Santinelli A, Mazzucchelli R, Colanzi P, Tinca A, Montironi R. Image processing, diagnostic information extraction and quantitative assessment in pathology. J Cell Mol Med 2002;6 (1):93–106.
• [15] Ta VT, Lezoray O, Elmoataz A, Schupp S. Graph-based tools for microscopic cellular image segmentation. Pattern Recognit 2009;42(6):1113–25.
• [16] Jung C, Kim C. Segmenting clustered nuclei using h-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 2010;57(10):2600–4.
• [17] Qi X, Xing F, Foran DJ, Yang L. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng 2012;59(3):754–65.
• [18] Vink J, Leeuwen MV, Deurzen CV, Haan G. Efficient nucleus detector in histopathology images. J Microsc 2013;249 (2):124–35.
• [19] Soille P. Morphological image analysis, principles and applications. Berlin: Springer; 2003.
• [20] Unser M. Sum and difference histograms for texture classification. IEEE Trans Pattern Anal Mach Intell 1986; PAMI-8(1):118–25.
• [21] Wagner P. Texture analysis. Handbook of computer vision and applications, vol. 2. San Diego Academic Press; 1999 [chapter 12].
• [22] Duda RO, Hart PE, Stork P. Pattern classification and scene analysis. New York: Wiley; 2003.
• [23] Schölkopf B, Smola A. Learning with kernels. Cambridge, MA: MIT Press; 2002.
• [24] Shu J, Fu H, Qiu G, Kaye P, Ilyas M. Segmenting overlapping cell nuclei in digital histopathology images. Proc. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2013.
• [25] Markiewicz T, Grala B, Kozlowski W, Osowski S. Computer system for cell counting in selected brain tumors at Ki-67 immunohistochemical staining. Anal Quant Cytol Histopathol 2010;32:323–32.
• [26] Markiewicz T, Osowski S, Patera J, Kozlowski W. Image processing for accurate recognition and counting of cells of the histological slides. Anal Quant Cytol Histopathol 2006;28:281–92.
• [27] Markiewicz T, Wisniewski P, Osowski S, Patera J, Kozlowski W, Koktysz R. Comparative analysis of the methods for accurate recognition of cells in the nuclei staining of the Ki- 67 in neuroblastoma and ER/PR status staining in breast cancer. Anal Quant Cytol Histopathol 2009;31:49–62.
• [28] Grala B, Markiewicz T, Kozlowski W, Osowski S, Slodkowska J, Papierz W. New automated image analysis method for assessment of Ki-67 labeling index in meningiomas. Folia Histochem Cytobiol 2009;47(4): 587–92.
• [29] Neuman U, Korzynska A, Lopez C, Lejeune M. Segmentation of stained lymphoma tissue section images. Information technology in biomedicine, vol. 2. Berlin/Heidelberg: Springer-Verlag; 2010.
• [30] Roszkowiak L, Korzynska A, Lejeune M, Bosch R, Lopez C. Improvements to segmentation method of stained lymphoma tissue section images. Proc. of the 9th International Conference on Computer Recognition Systems CORES 2015; 2016.
• [31] Korzynska A, Roszkowiak L, Lopez C, Bosch R, Lejeune M. Validation of various adaptive threshold method of segmentation applied to follicular lymphoma digital image stained with 3,30- diaminobenzidine & haematoxylin. Diagn Pathol 2013;8:48.
• [33] Wdowiak M, Markiewicz T, Osowski S, Swiderska Z, Patera J, Kozlowski W. Hourglass shapes in gray-level hit-or-miss transform for membrane segmentation in HER2/neu images. Proc. of 12th International Symposium on Mathematical Morphology (ISMM 2015), vol. 9083; 2015. pp. 3–14.
• [34] Swerdlow SH. WHO classification of tumours of haematopoietic and lymphoid tissue. Lyon: International Agency for Research on Cancer Press; 2008.
• [35] Swiderska Z, Korzynska A, Markiewicz T, Lorent M, Zak J, Wesolowska A, et al. Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for Ki-67 quantification in meningiomas. Anal Cell Pathol 2015;2015:1–15.
• [36] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24 (7):971–87.
• [37] Korzynska A, Roszkowiak L, Pijanowska D, Kozlowski W, Markiewicz T. The influence of the microscope lamp filament colour temperature on the process of digital images of histological slides acquisition standardization. Diagn Pathol 2014;9(1):13.
• [39] Jain AK. Fundamentals of digital image processing. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.; 1989.
• [40] Meijering E. A chronology of interpolation: from ancient astronomy to modern signal and image processing. Proc IEEE 2002;90(3):319–42.
• [41] Keys R. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 1981;29(6):1153–60.
• [42] Rowland SW. Computer implementation of image reconstruction formulas. Image reconstruction from projections: implementation and applications. Berlin: Springer-Verlag; 1979. p. 9–70.
• [43] Meijering EHW, Niessen WJ, Pluim JPW, Viergever MA. Medical image computing and computer-assisted intervention. MICCAI'99: Second International Conference; 1999.
• [44] Spitzbart A. A generalization of Hermite's interpolation formula. Am Math Mon 1960;21(1):21.
• [45] Twigg C. Catmull–Rom splines. Computer 2003;41(6):4–6.
• [46] Yuksel C, Schaefer S, Keyser J. On the parameterization of Catmull–Rom curves. SIAM/ACM Joint Conference on Geometric and Physical Modeling; 2009.
• [47] Unser M, Aldroubi A, Eden M. Fast B-spline transforms for continuous image representation and interpolation. IEEE Trans Pattern Anal Mach Intell 1991;13(3):277–85.
• [48] Hou H, Andrews H. Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 1978;26(6):508–17.
• [49] Scheiber E. On the interpolation trigonometric polynomial with an arbitrary even number of nodes. Proc. of 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing; 2011.
• [51] Immerkær J. Fast noise variance estimation. Comput Vision Image Underst 1996;64(2):300–2.
• [52] Korzynska A, Zychowicz M. A method of estimation of the cell doubling time on basis of the cell culture monitoring data. Biocybern Biomed Eng 2008;28(4):75–82.
• [53] Malik K, Smolka B. Modified bilateral filter for the restoration of noisy color images. Advanced concepts for intelligent vision systems; 2012.
• [54] Smolka B, Kusnik D. Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images. Signal Image Video Process 2015;9(1):49–56.
• [55] https://openseadragon.github.io [accessed 12.04.16].
• [56] http://www.bpmn-tool.com/en/tutorial/ [accessed 12.04.16].

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.