title: A Framework for 3D Vessel Analysis using Whole Slide Images of Liver Tissue Sections.
abstract: Three-dimensional (3D) high resolution microscopic images have high potential for improving the understanding of both normal and disease processes where structural changes or spatial relationship of disease features are significant. In this paper, we develop a complete framework applicable to 3D pathology analytical imaging, with an application to whole slide images of sequential liver slices for 3D vessel structure analysis. The analysis workflow consists of image registration, segmentation, vessel cross-section association, interpolation, and volumetric rendering. To identify biologically-meaningful correspondence across adjacent slides, we formulate a similarity function for four association cases. The optimal solution is then obtained by constrained Integer Programming. We quantitatively and qualitatively compare our vessel reconstruction results with human annotations. Validation results indicate a satisfactory concordance as measured both by region-based and distance-based metrics. These results demonstrate a promising 3D vessel analysis framework for whole slide images of liver tissue sections.
authors.affiliation: Department of Biomedical Informatics, Emory University, Atlanta, GA, USA yanhui.liang@emory.edu.
authors.forename: Yanhui
authors.initials: Y
authors.lastname: Liang
authors.affiliation: Department of Biomedical Informatics, Emory University, Atlanta, GA, USA fusheng.wang@emory.edu.
authors.forename: Fusheng
authors.initials: F
authors.lastname: Wang
authors.affiliation: Department of Pathology Leeds Teaching Hospitals NHS Trust Leeds Institute of Cancer and Pathology The University of Leeds, Leeds LS9 7TF, United Kingdom darrentreanor@nhs.net.
authors.forename: Darren
authors.initials: D
authors.lastname: Treanor
authors.affiliation: School of Computing, The University of Leeds, Leeds, LS2 9JT, United Kingdom D.R.Magee@leeds.ac.uk.
authors.forename: Derek
authors.initials: D
authors.lastname: Magee
authors.affiliation: Leeds Institute of Cancer and Pathology The University of Leeds, Leeds LS9 7TF, United Kingdom n.j.roberts@leeds.ac.uk.
authors.forename: Nick
authors.initials: N
authors.lastname: Roberts
authors.affiliation: Department of Computer Science, University of Brasília, Brasília, DF, Brazil teodoro@unb.br.
authors.forename: George
authors.initials: G
authors.lastname: Teodoro
authors.affiliation: Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA yangyang.zhu@emory.edu.
authors.forename: Yangyang
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
keywords: 3D Vessel Structure; Digital Pathology; Liver Pathology; Pathology Image Analysis; Whole Slide Imaging