An automated blur detection method for histological whole slide imaging

PloS One
Xavier Moles LopezChristine Decaestecker

Abstract

Whole slide scanners are novel devices that enable high-resolution imaging of an entire histological slide. Furthermore, the imaging is achieved in only a few minutes, which enables image rendering of large-scale studies involving multiple immunohistochemistry biomarkers. Although whole slide imaging has improved considerably, locally poor focusing causes blurred regions of the image. These artifacts may strongly affect the quality of subsequent analyses, making a slide review process mandatory. This tedious and time-consuming task requires the scanner operator to carefully assess the virtual slide and to manually select new focus points. We propose a statistical learning method that provides early image quality feedback and automatically identifies regions of the image that require additional focus points.

References

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Citations

Aug 1, 2014·Micron : the International Research and Review Journal for Microscopy·Yilun FanAndrew P Bradley
Jan 19, 2016·Computers in Biology and Medicine·Navid Noroozi, Ali Zakerolhosseini
Feb 16, 2019·Transplantation·Michelle A Wood-TrageserAnthony J Demetris
Jun 8, 2017·Advances in Anatomic Pathology·Zoya VolynskayaSylvia L Asa
Jan 11, 2020·Journal of Pathology Informatics·Timo KohlbergerMartin C Stumpe
Oct 17, 2020·Biochimica Et Biophysica Acta. Reviews on Cancer·David F SteinerCraig H Mermel
Jul 14, 2021·Journal of Microscopy·Prateek Katare, Sai Siva Gorthi

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Software Mentioned

scikit
QUEST Classification Tree
learn
image
Statistica
Python
Numba
SciPy

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