Automatic image quality assessment for digital pathology

Ali R.N. Avanaki, Kathryn S. Espig, Albert Xthona, Christian Lanciault, Tom R.L. Kimpe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Slide quality is an important factor in pathology workflow and diagnosis. We examine the extent of quality variations in digitized hematoxylin-eosin (H&E) slides due to variations and errors in staining and/or scanning (e.g., outof- focus blur & stitching). We propose two automatic quality estimators by adapting image quality assessment (IQA) methods that are originally developed for natural images. For the first estimator, we assume a gold-standard reference digital pathology slide is available. Quality of a given slide is estimated by comparing the slide to such a reference using a full-reference perceptual IQA method such as VIF (visual information fidelity) or SSIM (structural similarity metric). Our second estimator is based on IL-NIQE (integrated local natural image quality evaluator), a no-reference IQA, which we train using a set of artifact-free H&E high-power images (20× or 40×) from breast tissue. The first estimator (referenced) predicts marked quality reduction of images with simulated blurring as compared to the artifact-free originals used as references. The histograms of scores by the second estimator (no-reference) for images with artifact (blur, stitching, folded tissue, or air bubble artifacts) and for artifact-free images are highly separable. Moreover, the scores by the second estimator are correlated with the ratings given by a pathologist. We conclude that our approach is promising and further research is outlined for developing robust automatic quality estimators.

Original languageEnglish (US)
Title of host publicationBreast Imaging - 13th International Workshop, IWDM 2016, Proceedings
EditorsKristina Lang, Anders Tingberg, Pontus Timberg
PublisherSpringer Verlag
Pages431-438
Number of pages8
ISBN (Print)9783319415451
DOIs
StatePublished - Jan 1 2016
Event13th International Workshop on Breast Imaging, IWDM 2016 - Malmo, Sweden
Duration: Jun 19 2016Jun 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9699
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Breast Imaging, IWDM 2016
CountrySweden
CityMalmo
Period6/19/166/22/16

Keywords

  • Whole slide imaging (WSI)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Avanaki, A. R. N., Espig, K. S., Xthona, A., Lanciault, C., & Kimpe, T. R. L. (2016). Automatic image quality assessment for digital pathology. In K. Lang, A. Tingberg, & P. Timberg (Eds.), Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings (pp. 431-438). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9699). Springer Verlag. https://doi.org/10.1007/978-3-319-41546-8_54