Literature DB >> 36268060

Stain normalization in digital pathology: Clinical multi-center evaluation of image quality.

Nicola Michielli1, Alessandro Caputo2, Manuela Scotto1, Alessandro Mogetta1, Orazio Antonino Maria Pennisi3, Filippo Molinari1, Davide Balmativola4, Martino Bosco5, Alessandro Gambella6, Jasna Metovic6, Daniele Tota6, Laura Carpenito7,8, Paolo Gasparri9, Massimo Salvi1.   

Abstract

In digital pathology, the final appearance of digitized images is affected by several factors, resulting in stain color and intensity variation. Stain normalization is an innovative solution to overcome stain variability. However, the validation of color normalization tools has been assessed only from a quantitative perspective, through the computation of similarity metrics between the original and normalized images. To the best of our knowledge, no works investigate the impact of normalization on the pathologist's evaluation. The objective of this paper is to propose a multi-tissue (i.e., breast, colon, liver, lung, and prostate) and multi-center qualitative analysis of a stain normalization tool with the involvement of pathologists with different years of experience. Two qualitative studies were carried out for this purpose: (i) a first study focused on the analysis of the perceived image quality and absence of significant image artifacts after the normalization process; (ii) a second study focused on the clinical score of the normalized image with respect to the original one. The results of the first study prove the high quality of the normalized image with a low impact artifact generation, while the second study demonstrates the superiority of the normalized image with respect to the original one in clinical practice. The normalization process can help both to reduce variability due to tissue staining procedures and facilitate the pathologist in the histological examination. The experimental results obtained in this work are encouraging and can justify the use of a stain normalization tool in clinical routine.
© 2022 The Authors.

Entities:  

Keywords:  Digital pathology; H&E staining; Image quality; Qualitative score; Stain normalization

Year:  2022        PMID: 36268060      PMCID: PMC9577129          DOI: 10.1016/j.jpi.2022.100145

Source DB:  PubMed          Journal:  J Pathol Inform


  27 in total

1.  Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network.

Authors:  Juan Antonio Retamero; Jose Aneiros-Fernandez; Raimundo G Del Moral
Journal:  Arch Pathol Lab Med       Date:  2019-07-11       Impact factor: 5.534

Review 2.  Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.

Authors:  Jia-Mei Chen; Yan Li; Jun Xu; Lei Gong; Lin-Wei Wang; Wen-Lou Liu; Juan Liu
Journal:  Tumour Biol       Date:  2017-03

3.  Validation of a whole-slide image-based teleconsultation network.

Authors:  Alexi Baidoshvili; Nikolas Stathonikos; Gerard Freling; Jos Bart; Nils 't Hart; Jeroen van der Laak; Jan Doff; Bert van der Vegt; Philip M Kluin; Paul J van Diest
Journal:  Histopathology       Date:  2018-08-13       Impact factor: 5.087

Review 4.  A study about color normalization methods for histopathology images.

Authors:  Santanu Roy; Alok Kumar Jain; Shyam Lal; Jyoti Kini
Journal:  Micron       Date:  2018-08-01       Impact factor: 2.251

5.  A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution.

Authors:  Adnan Mujahid Khan; Nasir Rajpoot; Darren Treanor; Derek Magee
Journal:  IEEE Trans Biomed Eng       Date:  2014-06       Impact factor: 4.538

6.  Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images.

Authors:  Abhishek Vahadane; Tingying Peng; Amit Sethi; Shadi Albarqouni; Lichao Wang; Maximilian Baust; Katja Steiger; Anna Melissa Schlitter; Irene Esposito; Nassir Navab
Journal:  IEEE Trans Med Imaging       Date:  2016-04-27       Impact factor: 10.048

7.  The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

Authors:  Jeffrey Boschman; Hossein Farahani; Amirali Darbandsari; Pouya Ahmadvand; Ashley Van Spankeren; David Farnell; Adrian B Levine; Julia R Naso; Andrew Churg; Steven Jm Jones; Stephen Yip; Martin Köbel; David G Huntsman; C Blake Gilks; Ali Bashashati
Journal:  J Pathol       Date:  2021-11-06       Impact factor: 7.996

8.  Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks.

Authors:  Justin Tyler Pontalba; Thomas Gwynne-Timothy; Ephraim David; Kiran Jakate; Dimitrios Androutsos; April Khademi
Journal:  Front Bioeng Biotechnol       Date:  2019-11-01

9.  Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.

Authors:  Zaneta Swiderska-Chadaj; Thomas de Bel; Lionel Blanchet; Alexi Baidoshvili; Dirk Vossen; Jeroen van der Laak; Geert Litjens
Journal:  Sci Rep       Date:  2020-09-01       Impact factor: 4.379

10.  Quality control stress test for deep learning-based diagnostic model in digital pathology.

Authors:  Birgid Schömig-Markiefka; Alexey Pryalukhin; Wolfgang Hulla; Andrey Bychkov; Junya Fukuoka; Anant Madabhushi; Viktor Achter; Lech Nieroda; Reinhard Büttner; Alexander Quaas; Yuri Tolkach
Journal:  Mod Pathol       Date:  2021-06-24       Impact factor: 7.842

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