This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Region segmentation and classification in digital images of liver tissue Markos G. Tsipouras, Zoi Tsianou, Nikolaos Giannakeas, Alexandros T. Tzallas, Pinelopi Manousou, Epameinondas V. Tsianos,

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Fibrosis assessment in liver biopsies is currently based on semiquantitative staging scores i.e. Ishak. Collagen proportional area (CPA) assessment through digital image analysis have proven to be more accurate than semiquantitative scores since it can provide quantification of collagen. However, there are no methods presented in the literature for automated digital image analysis for CPA assessment. Introduction

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Development of an automated robust CPA assessment methodology through digital image analysis Three stage methodology a.Tissue/background separation (image segmentation) b.Tissue characterization (region classification) tissue muscle tissue, blood clots, structural collagen, stain, artifacts, fat c.CPA assessment Aim

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) 94 liver biopsies obtained from different patients picroSirious red stained photographed with a digital camera the images included several tissue and non-tissue areas, annotated by an expert pathologist Dataset

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF )

Tissue detection: a.3x3 pixels window b.average value for RGB c.clustering (K-means algorithm) Results to image regions Methodology [1/2]

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF )

Regions Classification: a.Calculation of several characteristics for each region (based on pixel values/color and region shape) Region Shape: {Area, Eccentricity, Diameter, Euler Number, Extent, Major & Minor Axis Length, Perimeter, Solidity} Pixel Color: {Mean/min/max Intensity for R/G/B channels} b.Region classification into several classes (decision tree algorithm) Classes: {Tissue, Muscle Tissue, Blood Clot, Structural Collagen, Dye, Artifact} Methodology [2/2]

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Structural Collagen Stain Blood Clot Artifact

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Results TissueMuscleBloodStr. Col.StainArtifactTotal% Tissue Muscle Blood Str. Col Stain Artifact Total Classification Accuracy: 83.5%

This research project has been co-financed by the European Union (European Regional Development Fund- ERDF) and Greek national funds through the Operational Program “THESSALY- MAINLAND GREECE AND EPIRUS ” of the National Strategic Reference Framework (NSRF ) Although CPA assessment through DIA has been introduced for more than two decades, and proven to be superior to traditional semiquantitative scores, it has not yet reached the everyday clinical practice. Development of simple to use and robust methodologies for all stages of image analysis can lead to wider spread of CPA assessment through DIA. Conclusions