RT Journal Article SR Electronic T1 Compound computer vision workflow for efficient and automated immunohistochemical analysis of whole slide images JF Journal of Clinical Pathology JO J Clin Pathol FD BMJ Publishing Group Ltd and Association of Clinical Pathologists SP jclinpath-2021-208020 DO 10.1136/jclinpath-2021-208020 A1 Michael Kyung Ik Lee A1 Madhumitha Rabindranath A1 Kevin Faust A1 Jennie Yao A1 Ariel Gershon A1 Noor Alsafwani A1 Phedias Diamandis YR 2022 UL http://jcp.bmj.com/content/early/2022/02/14/jclinpath-2021-208020.abstract AB Aims Immunohistochemistry (IHC) assessment of tissue is a central component of the modern pathology workflow, but quantification is challenged by subjective estimates by pathologists or manual steps in semi-automated digital tools. This study integrates various computer vision tools to develop a fully automated workflow for quantifying Ki-67, a standard IHC test used to assess cell proliferation on digital whole slide images (WSIs).Methods We create an automated nuclear segmentation strategy by deploying a Mask R-CNN classifier to recognise and count 3,3′-diaminobenzidine positive and negative nuclei. To further improve automation, we replaced manual selection of regions of interest (ROIs) by aligning Ki-67 WSIs with corresponding H&E-stained sections, using scale-invariant feature transform (SIFT) and a conventional histomorphological convolutional neural networks to define tumour-rich areas for quantification.Results The Mask R-CNN was tested on 147 images generated from 34 brain tumour Ki-67 WSIs and showed a high concordance with aggregate pathologists’ estimates ( assessors; r=0.9750). Concordance of each assessor’s Ki-67 estimates was higher when compared with the Mask R-CNN than between individual assessors (ravg=0.9322 vs 0.8703; p=0.0213). Coupling the Mask R-CNN with SIFT-CNN workflow demonstrated ROIs can be automatically chosen and partially sampled to improve automation and dramatically decrease computational time (average: 88.55–19.28 min; p<0.0001).Conclusions We show how innovations in computer vision can be serially compounded to automate and improve implementation in clinical workflows. Generalisation of this approach to other ancillary studies has significant implications for computational pathology.Data are available in a public, open access repository. Data are available upon reasonable request. (https://bitbucket.org/diamandislabii/mask-rcnn/src/master/)