Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

High Resolution Optical Imaging Techniques for Rapid Assessment of Breast Cancer

Breast cancer is the most prevalent and deadly cancer among women worldwide. The current standard for breast lesion diagnosis is histologic assessment with hematoxylin and eosin (H&E) staining. Histology has high diagnostic accuracy, but requires extensive time and resources to perform. The objective of this work was to improve diagnosis of early breast cancers by developing approaches to rapidly image and characterize neoplastic tissue and the tumor microenvironment in high resolution optical images. Confocal fluorescence microscopy can image optical sections of tissue without the need for extensive tissue processing. Three studies were performed to evaluate if confocal microscopy images contain sufficient information to identify neoplasia in breast tissue. In a 31 patient study, five pathologists identified neoplasia with high accuracy in confocal and histologic images. In another study, an expert pathologist estimated tumor cellularity in core biopsies with moderate agreement between confocal and histologic images. In a third study, an expert pathologist assigned diagnoses and grades to neoplastic tissue in confocal and histologic images. Limitations of these studies include recruitment of patients at a single center and data assessment by a single reader in two of three studies. Visual assessment for cancer diagnosis is limited by the potential for inter- and intra-observer error. Using a computerized algorithm to segment and quantify architectural features of breast ducts and nuclei, a decision-tree model was developed that classified confocal images of breast tissue sites as neoplastic or non-neoplastic with an overall accuracy of 90%. Another computerized algorithm was developed to segment adipocytes in confocal images and results showed significant differences in phenotypic properties of adipocytes adjacent to neoplastic and non-neoplastic tissue. High resolution microendoscopy (HRME) can be used to rapidly acquire images at a lower cost than confocal microscopy. In a study evaluating HRME and two approaches to improve image contrast, results demonstrated that HRME with structured illumination yields images with high contrast relative to HRME with standard illumination. The unique contribution of these results is the characterization of qualitative and quantitative criteria to evaluate breast tissue and classify neoplasia in optical images, although recognition of invasive lobular carcinoma was limited. The criteria developed in this research may be applied to further development of techniques for objective classification and diagnosis of breast cancer in optical images.

AUTOMATIC SCENE COMPARISON AND MATCHING IN MULTIMODAL CYTOPATHOLOGICAL MICROSCOPIC IMAGES

title = {{A}utomatic {S}cene {C}omparison and {M}atching in {M}ultimodal {C}ytopathological {M}icroscopic {I}mages}, booktitle = {IEEE International Conferene on Image Processing. ICIP 2005}, publisher = {IEEE}, year = 2005, volume = 1, pages = {1145--1148} © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. document created on: March 3, 2006 created from file: paper.tex cover page automatically created withCoverPage.sty (available at your favourite CTAN mirror

Queensland University of Technology: Handbook 2012

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT

Queensland University of Technology: Handbook 2013

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT