Markos G Tsipouras1, Nikolaos Giannakeas2, Alexandros T Tzallas3, Zoe E Tsianou4, Pinelopi Manousou5, Andrew Hall6, Ioannis Tsoulos7, Epameinondas Tsianos8. 1. Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece. Electronic address: tsipouras@teiep.gr. 2. Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece. Electronic address: giannakeas@teiep.gr. 3. Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece. Electronic address: tzallas@teiep.gr. 4. Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece. Electronic address: z.tsianou@gmail.com. 5. Liver Unit, St Mary's Hospital, Imperial College NHS Trust, London, UK. Electronic address: pinelopi.manousou@imperial.nhs.uk. 6. Department of Histopathology, UCL Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2QG, UK. Electronic address: andrewhall1@nhs.net. 7. Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece. Electronic address: itsoulos@teiep.gr. 8. Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece. Electronic address: etsianos@uoi.gr.
Abstract
BACKGROUND AND OBJECTIVE: Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. METHODS: The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. RESULTS: For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. CONCLUSIONS: The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time.
BACKGROUND AND OBJECTIVE: Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. METHODS: The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. RESULTS: For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. CONCLUSIONS: The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time.
Authors: Faidon-Marios Laskaratos; Ana Levi; Gert Schwach; Roswitha Pfragner; Andrew Hall; Dong Xia; Conrad von Stempel; Josephine Bretherton; Kessarin Thanapirom; Sarah Alexander; Olagunju Ogunbiyi; Jennifer Watkins; Tu Vinh Luong; Christos Toumpanakis; Dalvinder Mandair; Martyn Caplin; Krista Rombouts Journal: Front Oncol Date: 2021-02-24 Impact factor: 6.244
Authors: Roberta Forlano; Benjamin H Mullish; Nikolaos Giannakeas; James B Maurice; Napat Angkathunyakul; Josephine Lloyd; Alexandros T Tzallas; Markos Tsipouras; Michael Yee; Mark R Thursz; Robert D Goldin; Pinelopi Manousou Journal: Clin Gastroenterol Hepatol Date: 2019-12-27 Impact factor: 11.382