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Automatic Image Quality Assessment for Digital Pathology

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9699)

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., out-of-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.

Keywords

  • Whole slide imaging (WSI)

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Notes

  1. 1.

    The actual blur may be caused by a different kernel and is a function of scanner modulation transfer function, (auto-)focus quality and method, and vary with location, due to specimen height variations.

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Acknowledgement

Ali Avanaki would like to thank Eddie Knippel for his comments.

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Correspondence to Ali R. N. Avanaki .

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Avanaki, A.R.N., Espig, K.S., Xthona, A., Lanciault, C., Kimpe, T.R.L. (2016). Automatic Image Quality Assessment for Digital Pathology. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_54

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  • DOI: https://doi.org/10.1007/978-3-319-41546-8_54

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