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  • 标题:Hyper-Stain Inspector: A Framework for Robust Registration and Localised Co-Expression Analysis of Multiple Whole-Slide Images of Serial Histology Sections
  • 本地全文:下载
  • 作者:Nicholas Trahearn ; David Epstein ; Ian Cree
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2017
  • 卷号:7
  • 期号:1
  • DOI:10.1038/s41598-017-05511-w
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:In this paper, we present a fast method for registration of multiple large, digitised whole-slide images (WSIs) of serial histology sections. Through cross-slide WSI registration, it becomes possible to select and analyse a common visual field across images of several serial section stained with different protein markers. It is, therefore, a critical first step for any downstream co-localised cross-slide analysis. The proposed registration method uses a two-stage approach, first estimating a fast initial alignment using the tissue sections' external boundaries, followed by an efficient refinement process guided by key biological structures within the visual field. We show that this method is able to produce a high quality alignment in a variety of circumstances, and demonstrate that the refinement is able to quantitatively improve registration quality. In addition, we provide a case study that demonstrates how the proposed method for cross-slide WSI registration could be used as part of a specific co-expression analysis framework.
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