A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques

Computer Methods and Programs in Biomedicine

Volumn 140, Issue , 2017, Pages 61-68

  Tsipouras, Markos G ^a,b   Giannakeas, Nikolaos ^a,b   Tzallas, Alexandros T ^a,b   Tsianou, Zoe E ^a   Manousou, Pinelopi ^c   Hall, Andrew ^d   Tsoulos, Ioannis ^b   Tsianos, Epameinondas ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

^b Epirus Institute of Technology Arta   (Greece)

^c IMPERIAL COLLEGE HEALTHCARE NHS TRUST   (United Kingdom)

^d ROYAL FREE AND UNIVERSITY COLLEGE MEDICAL SCHOOL   (United Kingdom)

Author keywords

Classification; Clustering; Collagen proportional area; Liver biopsy image analysis; Machine learning techniques

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOPSY; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; COLLAGEN; EXTRACTION; IMAGE ANALYSIS; IMAGE PROCESSING; LEARNING ALGORITHMS; LEARNING SYSTEMS;

CLASSIFICATION ALGORITHM; CLINICAL PRACTICES; CLUSTERING; CORRELATION COEFFICIENT; IMAGE ANALYSIS TECHNIQUES; LIVER BIOPSIES; MACHINE LEARNING TECHNIQUES; TISSUE SEPARATIONS;

TISSUE;

COLLAGEN;

ARTICLE; AUTOMATION; BLOOD CLOT; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; COLLAGEN PROPORTIONAL AREA EXTRACTION; CONTROLLED STUDY; EXTRACTION; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE PROCESSING; K NEAREST NEIGHBOR; LIVER BIOPSY; LIVER FIBROSIS; MACHINE LEARNING; MUSCLE TISSUE; NERVE CELL NETWORK; RANDOM FOREST; SUPPORT VECTOR MACHINE; BIOPSY; CHRONIC HEPATITIS C; LIVER; METABOLISM; PATHOLOGY;

AUTOMATION; BIOPSY; COLLAGEN; HEPATITIS C, CHRONIC; HUMANS; LIVER; MACHINE LEARNING;

EID: 85007019196     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2016.11.012     Document Type: Article

References (26)

1. [1] Averhoff, F.M., Glass, N., Holtzman, D., Global burden of hepatitis C: considerations for healthcare providers in the United States. Clin. Infect. Dis. 55:Jul(1) (2012), S10–S15.
2. [2] Calvaruso, V., Burroughs, A.K., Standish, R., Manousou, P., Grillo, F., Leandro, G., et al. Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient. Hepatology 49:Apr (2009), 1236–1244.
3. [3] Knodell, R.G., Ishak, K.G., Black, W.C., Chen, T.S., Craig, R., Kaplowitz, N., et al. Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis. Hepatology 1:Sep-Oct (1981), 431–435.
4. [4] Scheuer, P.J., Classification of chronic viral hepatitis: a need for reassessment. J Hepatol 13:Nov (1991), 372–374.
5. [5] Bedossa, P., Poynard, T., An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology 24:Aug (1996), 289–293.
6. [6] Ishak, K., Baptista, A., Bianchi, L., Callea, F., De Groote, J., Gudat, F., et al. Histological grading and staging of chronic hepatitis. J. Hepatol. 22:Jun (1995), 696–699.
7. [7] Manousou, P., Dhillon, A.P., Isgro, G., Calvaruso, V., Luong, T.V., Tsochatzis, E., et al. Digital image analysis of liver collagen predicts clinical outcome of recurrent hepatitis C virus 1 year after liver transplantation. Liver Transpl. 17:Feb (2011), 178–188.
8. [8] Raftopoulos, S.C., George, J., Bourliere, M., Rossi, E., de Boer, W.B., Jeffrey, G.P., et al. Comparison of noninvasive models of fibrosis in chronic hepatitis B. Hepatol. Int. 6:Apr (2012), 457–467.
9. [9] Huang, Y., de Boer, W.B., Adams, L.A., MacQuillan, G., Rossi, E., Rigby, P., et al. Image analysis of liver collagen using sirius red is more accurate and correlates better with serum fibrosis markers than trichrome. Liver Int. 33:Sep (2013), 1249–1256.
10. [10] Huang, Y., de Boer, W.B., Adams, L.A., MacQuillan, G., Bulsara, M.K., Jeffrey, G.P., Image analysis of liver biopsy samples measures fibrosis and predicts clinical outcome. J. Hepatol. 61 (Jul 2014), 22–27.
11. [11] Nakabayashi, H., Takamatsu, S., Tsujii, H., Okamoto, Y., Nakano, H., Yamada, E., et al. Collagen content of liver biopsy specimens in patients with chronic hepatitis. Int. Hepatol. Commun. 4 (1996), 311–315 4//.
12. [12] Calvaruso, V., Dhillon, A.P., Tsochatzis, E., Manousou, P., Grillo, F., Germani, G., et al. Liver collagen proportionate area predicts decompensation in patients with recurrent hepatitis C virus cirrhosis after liver transplantation. J. Gastroenterol. Hepatol. 27:Jul (2012), 1227–1232.
13. [13] Campos, C.F., Paiva, D.D., Perazzo, H., Moreira, P.S., Areco, L.F., Terra, C., et al. An inexpensive and worldwide available digital image analysis technique for histological fibrosis quantification in chronic hepatitis C. J. Viral. Hepat. 21:Mar (2014), 216–222.
14. [14] Kage, M., Shimamatu, K., Nakashima, E., Kojiro, M., Inoue, O., Yano, M., Long-term evolution of fibrosis from chronic hepatitis to cirrhosis in patients with hepatitis C: morphometric analysis of repeated biopsies. Hepatology 25:Apr (1997), 1028–1031.
15. [15] Pilette, C., Rousselet, M.C., Bedossa, P., Chappard, D., Oberti, F., Rifflet, H., et al. Histopathological evaluation of liver fibrosis: quantitative image analysis vs semi-quantitative scores. Comparison with serum markers. J. Hepatol. 28:Mar (1998), 439–446.
16. [16] Masseroli, M., Caballero, T., O'Valle, F., Del Moral, R.M., Perez-Milena, A., Del Moral, R.G., Automatic quantification of liver fibrosis: design and validation of a new image analysis method: comparison with semi-quantitative indexes of fibrosis. J. Hepatol. 32:Mar (2000), 453–464.
17. [17] Kurita, T., Otsu, N., Abdelmalek, N., Maximum likelihood thresholding based on population mixture models. Pattern Recognit. 25 (1992), 1231–1240 10//.
18. [18] Kittler, J., Illingworth, J., Minimum error thresholding. Pattern Recognit. 19 (1986), 41–47 /01/01 1986.
19. [19] Caballero, T., Perez-Milena, A., Masseroli, M., O'Valle, F., Salmeron, F.J., Del Moral, R.M., et al. Liver fibrosis assessment with semiquantitative indexes and image analysis quantification in sustained-responder and non-responder interferon-treated patients with chronic hepatitis C. J. Hepatol. 34:May (2001), 740–747.
20. [20] Xu, S., Wang, Y., Tai, D.C., Wang, S., Cheng, C.L., Peng, Q., et al. qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients. J. Hepatol. 61:Aug (2014), 260–269.
21. [21] MacQueen, J., Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, Berkeley, Calif., 1967, 281–297.
22. [22] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms. 1981, Kluwer Academic Publishers.
23. [23] Bishop, C.M., Neural Networks for Pattern Recognition. 1995, Oxford University Press, Inc.
24. [24] Demuth, H.B., Beale, M.H., MathWorks, I., MATLAB Neural Network Toolbox: User's Guide. 1992, Mathworks, Incorporated.
25. [25] Kantardzic, M., Data Mining: Concepts, Models, Methods and Algorithms. 2002, John Wiley &; Sons, Inc.
26. [26] Steichen, T.J., Cox, N.J., A note on the concordance correlation coefficient. Stata J. 2 (2002), 183–189.