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High-throughput fat quantifications of hematoxylin-eosin stained liver histopathological images based on pixel-wise clustering


Besides diagnosis of fatty liver disease (FLD) using multiple medical imaging techniques in clinic, accurate fat quantification of liver tissue slice, especially the fat droplets measurement, is still a critical indicator in related pathological researches. Stained by hematoxylin-eosin (HE), different tissue components with different colors need to be identified and measured manually in conventional approaches. Automated liver fat quantification of HE stained images remains challenging because forms and distributions of fat are extremely irregular with no clear boundaries, especially in conducting high-throughput analysis which demands quick processing and higher accuracy for the reference of pathologists. To solve this problem, we propose an automated liver fat quantifications pipeline of HE stained images based on pixel-wise clustering, which firstly extracts high-relevant pixel-level features with color mode transformation, then locates boundaries between nuclei, fat and other components by clustering image pixels in an unsupervised mode, and finally identifies indicative fat droplets based on a set of morphological criteria. The pipeline was verified in analysis of multifold fatty liver treatment assays, with experimental results showing high accuracy and adaptability in fat droplets quantification despite data variance. Quantitative indicators provide a reliable evidence for relevant pathological researches or therapy selection, in which number and average area of indicative fat droplets increased sharply in severe and moderate-grade FLD respectively. Those indicators might be utilized as surrogate biomarkers for further researches.

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  1. 1

    Fan J G, Zhou Q, Wo Q H. Effect of body weight mass and its change on the incidence of nonalcoholic fatty liver disease (in Chinese). Zhonghua Gan Zang Bing Za Zhi, 2010, 18: 676–679

  2. 2

    Jain D, Nayak N C, Saigal S. Hepatocellular carcinoma in nonalcoholic fatty liver cirrhosis and alcoholic cirrhosis: risk factor analysis in liver transplant recipients. Eur J Gastroentero Hepatol, 2012, 24: 840–848

  3. 3

    Stewart S, Jones D, Day C P. Alcoholic liver disease: new insights into mechanisms and preventative strategies. Trends Mol Med, 2001, 7: 408–413

  4. 4

    Shaker M, Tabbaa A, Albeldawi M, et al. Liver transplantation for nonalcoholic fatty liver disease: new challenges and new opportunities. World J Gastroentero, 2014, 20: 5320–5330

  5. 5

    Layer G, Zuna I, Lorenz A, et al. Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution. Ultrasonic Imag, 1990, 12: 171–188

  6. 6

    Marko L, Deike H, Nancy N, et al. Non-invasive quantification of white and brown adipose tissues and liver fat content by computed tomography in mice. Plos One, 2012, 7: e37026

  7. 7

    Thomsen C, Becker U, Winkler K, et al. Quantification of liver fat using magnetic resonance spectroscopy. Magn Reson Imag, 1994, 12: 487–495

  8. 8

    Gurcan M N, Boucheron L E, Can A, et al. Histopathological image analysis: a review. IEEE Rev Biomed Eng, 2009, 2: 147–171

  9. 9

    Belsare A D, Mushrif M M. Histopathological image analysis using image processing techniques: an overview. Signal Image Process, 2012, 3: 101–109

  10. 10

    Schneider C A, Rasband W S, Eliceiri K W. NIH image to Image J: 25 years of image analysis. Nature Method, 2012, 9: 671–675

  11. 11

    Qi X, Xing F, Foran D J, et al. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng, 2012, 59: 754–765

  12. 12

    Zhang K, Zhang L, Song H, et al. Active contours with selective local or global segmentation: a new formulation and level set method. Image Vision Comput, 2010, 28: 668–676

  13. 13

    Tosun A B, Gunduz-Demir C. Graph run-length matrices for histopathological image segmentation. IEEE Trans Med Imag, 2011, 30: 721–732

  14. 14

    Simsek A C, Tosun A B, Aykanat C, et al. Multilevel segmentation of histopathological images using cooccurrence of tissue objects. IEEE Trans Biomed Eng, 2012, 59: 1681–1690

  15. 15

    Al-Kadi O S. Texture measures combination for improved meningioma classification of histopathological images. Pattern Recogn, 2010, 43: 2043–2053

  16. 16

    Hui K, Gurcan M, Belkacem-Boussaid K. Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imag, 2011, 30: 1661–1677

  17. 17

    Qu A P, Chen J M, Wang L W, et al. Segmentation of Hematoxylin-Eosin stained breast cancer histopathological images based on pixel-wise SVM classifier. Sci China Inf Sci, 2015, 58: 092105

  18. 18

    Subashini T S, Ramalingam V, Palanivel S. Breast mass classification based on cytological patterns using RBFNN and SVM. Expert Syst Appl, 2009, 36: 5284–5290

  19. 19

    Zarella M D, Breen D E, Plagov A, et al. An optimized color transformation for the analysis of digital images of hematoxylin and eosin stained slides. J Pathol Inf, 2015, 6: 33

  20. 20

    Vahadane A, Sethi A. Towards generalized nuclear segmentation in histological images. In: Proceedings of IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), Chania, 2013. 7789: 1–4

  21. 21

    Kiernan J A. Histological and Histochemical Methods: Theory and Practice. 4th ed. Bloxham: Scion, 2008

  22. 22

    Sun T N, Neurvo Y. Detail-preserving median based filters in image processing. Pattern Recogn Lett, 1994, 15: 341–347

  23. 23

    van Vliet L J, Young L T, Verbeek PW. Recursive Gaussian derivative filters. In: Proceedings of the 14th International Conference on Pattern Recognition (ICPR), Brisbane, 1998

  24. 24

    Estrada F J, Jepson A D. Benchmarking image segmentation algorithms. Int J Comput Vis, 2009, 85: 167–181

  25. 25

    Lloyd S P. Least square quantization in PCM. IEEE Trans Inf Theory, 1982, 28: 129–137

  26. 26

    Malpica N, de Solorzano C O, Vaquero J J, et al. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry, 1997, 28: 289–297

  27. 27

    Karvelis P S, Tzallas A T, Fotiadis D I, et al. A multichannel watershed-based segmentation method for multispectral chromosome classification. IEEE Trans Med Imag, 2008, 27: 697–708

  28. 28

    Reddy J K, Rao M S. Lipid metabolism and liver inflammation. II. Fatty liver disease and fatty acid oxidation. Am J Physiol Gastrointest Liver Physiol, 2006, 290: 852–858

  29. 29

    Adams L A, Lymp J F, St Sauver J, et al. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology, 2005, 129: 113–121

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This work was supported by National Natural Science Foundation of China (Grant No. 61501121), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (Grant No. (2015)1098), Provincial Science Foundation, Fujian Provincial Department of Science and Technology (Grant No. 2015J05145), and Provincial Research Funds for Innovative Youth, Fujian Provincial Department of Education (Grant No. JA14084).

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Correspondence to Peng Shi.

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Shi, P., Chen, J., Lin, J. et al. High-throughput fat quantifications of hematoxylin-eosin stained liver histopathological images based on pixel-wise clustering. Sci. China Inf. Sci. 60, 092108 (2017).

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  • fatty liver disease
  • liver histopathological images
  • hematoxylin-eosin staining
  • liver fat quantification
  • image segmentation
  • clustering