Article Text
Abstract
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.
- brain neoplasms
- computers
- molecular
- diagnostic techniques and procedures
- immunohistochemistry
- image processing
- computer-assisted
Data availability statement
Data are available in a public, open access repository. Data are available upon reasonable request. (https://bitbucket.org/diamandislabii/mask-rcnn/src/master/)
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- brain neoplasms
- computers
- molecular
- diagnostic techniques and procedures
- immunohistochemistry
- image processing
- computer-assisted
Data availability statement
Data are available in a public, open access repository. Data are available upon reasonable request. (https://bitbucket.org/diamandislabii/mask-rcnn/src/master/)
Footnotes
Handling editor Runjan Chetty.
Twitter @pdiamandisii
MKIL, MR and KF contributed equally.
Contributors KF, MR, MKIL and PD conceived the idea and approach. KF developed the computational workflow. MR, MKIL, JY and PD annotated and generated the training and validation image set, and interpreted the IHC data. NA, AG and PD assisted with human evaluation of IHC images. KF, MR, MKIL and PD wrote the manuscript with input from all other authors. PD acted as the guarantor for this paper.
Funding Funding support is provided by the Terry Fox Research Institute New Investigator Award, Cancer Research Society, the Canadian Institute for Health Research and the Princess Margaret Cancer Foundation. MKIL is supported by Frederick Banting and Charles Best Canada Graduate Scholarships (CGS-M).
Competing interests None declared.
Provenance and peer review Not commissioned; internally peer reviewed.
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