Tumour Localisation in Histopathology Images

Immunohistochemical (IHC) assessment in cancer research is important for understanding the distribution and localisation of biomarkers at the cellular level. However currently IHC analyses are predominantly performed manually, increasing workloads and introducing inter- and intra-observer variability. Automation shows great potential in clinical research to reduce pathologists' workloads and speed up cancer research in large clinical studies. Whilst recent advancements in digital pathology have enabled IHC measurements to be performed automatically, the acquisition of manual annotations of tumours in scanned digital slides is still a limiting factor. In this thesis, an automated solution to tumour localisation is explored with the aim of replacing manual annotations. As an exemplar, human breast tissue microarrays stained with estrogen receptor are considered.<br/><br/>Methods for automated tumour localisation are described with a focus on capturing structural information in tissue by adopting superpixel properties in a rotation invariant manner, suitable for histopathology images. To incorporate essential contextual information, methods which utilise posterior tumour probabilities in an iterative manner are proposed. Results showed pixel-level agreements between automated and manual tumour segmentation masks (κ=0.811) approach inter-rater agreement between expert pathologists (κ=0.908). A large proportion of disagreements between automated and manual segmentations were shown to correlate to minor discrepancies, inconsequential for IHC assessment. IHC scores extracted from automated and manual tumour segmentation masks showed strong agreements (Allred: κˆ=0.911; Quickscore: κˆ=0.922), demonstrating the potential of automation in clinical practice across large clinical trials.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of Dundee

Year dc:date.issued

2015

Author dc:creator

• Akbar, Shazia

Advisor dc:contributor.advisor

• McKenna, Stephen

• Tumour localisation
• Immunohistochemistry
• Breast cancer
• Histopathology
• Computer vision
• Superpixels
• Spin-context

Language dc:language

eng

Identifier

oai:discovery.dundee.ac.uk:studenttheses/c282ab9c-5776-400f-8440-f5ac9cf2f4ba

OAI identifier oai:identifier

oai:discovery.dundee.ac.uk:studenttheses/c282ab9c-5776-400f-8440-f5ac9cf2f4ba