Open AccessHistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides
Author(s) -
Andrew Janowczyk,
Ren Zuo,
Hannah Gilmore,
Michael D. Feldman,
Anant Madabhushi
Publication year - 2019
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.18.00157
Subject(s) - digital pathology , computer science , workflow , graphical user interface , visualization , digitization , computer vision , artificial intelligence , flagging , pattern recognition (psychology) , information retrieval , cartography , database , programming language , geography
Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis.