Use this URL to cite or link to this record in EThOS:
Title: Tumour localisation in histopathology images
Author: Akbar, Shazia
Awarding Body: University of Dundee
Current Institution: University of Dundee
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Access from Institution:
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. 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.
Supervisor: McKenna, Stephen Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Tumour localisation ; Immunohistochemistry ; Breast cancer ; Histopathology ; Computer vision ; Superpixels ; Spin-context