Literature DB >> 21245003

Automated segmentation of cells with IHC membrane staining.

Elisa Ficarra1, Santa Di Cataldo, Andrea Acquaviva, Enrico Macii.   

Abstract

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.
© 2011 IEEE

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Year:  2011        PMID: 21245003     DOI: 10.1109/TBME.2011.2106499

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  3 in total

1.  Digital separation of diaminobenzidine-stained tissues via an automatic color-filtering for immunohistochemical quantification.

Authors:  Rong Fu; Xiaomian Ma; Zhaoying Bian; Jianhua Ma
Journal:  Biomed Opt Express       Date:  2015-01-15       Impact factor: 3.732

2.  Coupled Segmentation of Nuclear and Membrane-bound Macromolecules through Voting and Multiphase Level Set.

Authors:  Hang Chang; Quan Wen; Bahram Parvin
Journal:  Pattern Recognit       Date:  2015-03-01       Impact factor: 7.740

Review 3.  Recent advances in morphological cell image analysis.

Authors:  Shengyong Chen; Mingzhu Zhao; Guang Wu; Chunyan Yao; Jianwei Zhang
Journal:  Comput Math Methods Med       Date:  2012-01-09       Impact factor: 2.238

  3 in total

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