Literature DB >> 25360444

Appearance Normalization of Histology Slides.

Marc Niethammer, David Borland, J S Marron, John Woosley, Nancy E Thomas.   

Abstract

This paper presents a method for automatic color and intensity normalization of digitized histology slides stained with two different agents. In comparison to previous approaches, prior information on the stain vectors is used in the estimation process, resulting in improved stability of the estimates. Due to the prevalence of hematoxylin and eosin staining for histology slides, the proposed method has significant practical utility. In particular, it can be used as a first step to standardize appearances across slides, that is very effective at countering effects due to differing stain amounts and protocols, and to slide fading. The approach is validated using synthetic experiments and 13 real datasets.

Entities:  

Year:  2010        PMID: 25360444      PMCID: PMC4211434          DOI: 10.1007/978-3-642-15948-0_8

Source DB:  PubMed          Journal:  Mach Learn Med Imaging


  1 in total

1.  Quantification of histochemical staining by color deconvolution.

Authors:  A C Ruifrok; D A Johnston
Journal:  Anal Quant Cytol Histol       Date:  2001-08       Impact factor: 0.302

  1 in total
  8 in total

Review 1.  Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review.

Authors:  Asha Das; Madhu S Nair; S David Peter
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

2.  Staining correction in digital pathology by utilizing a dye amount table.

Authors:  Pinky A Bautista; Yukako Yagi
Journal:  J Digit Imaging       Date:  2015-06       Impact factor: 4.056

Review 3.  Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

Authors:  Madeleine S Durkee; Rebecca Abraham; Marcus R Clark; Maryellen L Giger
Journal:  Am J Pathol       Date:  2021-06-12       Impact factor: 5.770

4.  Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining.

Authors:  Yves-Rémi Van Eycke; Justine Allard; Isabelle Salmon; Olivier Debeir; Christine Decaestecker
Journal:  Sci Rep       Date:  2017-02-21       Impact factor: 4.379

Review 5.  Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review.

Authors:  Laya Jose; Sidong Liu; Carlo Russo; Annemarie Nadort; Antonio Di Ieva
Journal:  J Pathol Inform       Date:  2021-11-03

Review 6.  Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.

Authors:  Xiaoliang Xie; Xulin Wang; Yuebin Liang; Jingya Yang; Yan Wu; Li Li; Xin Sun; Pingping Bing; Binsheng He; Geng Tian; Xiaoli Shi
Journal:  Front Oncol       Date:  2021-11-10       Impact factor: 6.244

Review 7.  Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers.

Authors:  Alex Ngai Nick Wong; Zebang He; Ka Long Leung; Curtis Chun Kit To; Chun Yin Wong; Sze Chuen Cesar Wong; Jung Sun Yoo; Cheong Kin Ronald Chan; Angela Zaneta Chan; Maribel D Lacambra; Martin Ho Yin Yeung
Journal:  Cancers (Basel)       Date:  2022-08-03       Impact factor: 6.575

8.  Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype.

Authors:  Heather D Couture; Lindsay A Williams; Joseph Geradts; Sarah J Nyante; Ebonee N Butler; J S Marron; Charles M Perou; Melissa A Troester; Marc Niethammer
Journal:  NPJ Breast Cancer       Date:  2018-09-03
  8 in total

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