Automated quantitative and qualitative analysis of neuroblastoma cancer tissue

University of Technology, Sydney. Faculty of Engineering and Information Technology.The goal of this thesis is to develop an innovative Computer Aided Diagnosis (CAD) system for the common deadly infant cancer of Neuroblastoma. Neuroblastoma accounts for more than 15% of childhood cancer deaths, and it has the lowest survival rate among the paediatric cancers in Australia. In quantitative analysis the total number of different regions of interest are counted, and qualitative analysis determines abnormalities within the tumour. Quantitative and qualitative analysis of tumor samples under the microscope is one of the key markers used by pathologists to determine the aggressiveness of the cancer, and consequently its therapy. Because of the variety of the histological region types and histological structures in the tissue, analyzing them under the microscope is a tedious and error-prone task for pathologists. The negative effects of inaccurate quantitative and qualitative analysis have led to an urgent call from pathologists for accurate, consistent and automated approaches. Computer Aided Diagnosis (CAD) is an automated cancer diagnostic and prognostic system which enhances the ability of pathologists in the quantitative and qualitative analysis of tumor tissues. However, there are four main issues with developing a CAD system for pathology labs: First is the fluctuating quality of the histological images. Second is a wide range of different types of histological regions and histological structures with complex morphology each adopting a specific algorithm. Third is overlapping cells which decrease the accuracy of quantitative analysis. Fourth is a lack of utility for pathology labs when they do not follow an appropriate clinical prognosis scheme. Moreover, most of the proposed CAD systems perform either quantitative or quantitative analysis and only very few of them manipulate both types of analysis on the cancerous tumor tissue. This thesis aims to address the issues raised by developing an innovative CAD system that assists pathologists in determining a more appropriate prognosis for the leading infant cancer of Neuroblastoma. The CAD will automatically perform quantitative and qualitative analysis on images of tumor tissue to extract specific histological regions and histological structures which are used for determining the prognosis for Neuroblastoma. This thesis has four main contributions. Contribution 1 develops novel algorithms to enhance the quality of histological images by reducing the wide range of intensity variations. Contribution 2 proposes a series of segmentation algorithms for extracting different types of histological regions and histological structures. Contribution 3 addresses the issue of overlapping cells by developing algorithms for splitting them into single cells. Contribution 4 grades the aggressiveness level of neuroblastoma tumor by developing a prognosis decision engine. The main outcomes of the proposed CAD system in this thesis are a series of novel algorithms for enhancing the quality of the histological images and for segmenting histological regions and histological structures of interests, introducing a prognosis decision engine for grading a neuroblastoma tumor based on a well established histopathological scheme, facilitating the process of prognosis and tumor classification by performing accurate and consistent quantitative and qualitative tissue analysis, and enhancing digital pathology by incorporating a digital and automated system in the work flow of pathologists. The performance of all the developed algorithms in this thesis in terms of correctly extracting histological regions, histological structures and grading the level of tumor aggressiveness, is evaluated by a pathologist from the department of histopathology in the Children's Hospital at Westmead, Sydney. Moreover, all the results are compared with state of the art methods. The results indicate that the algorithms proposed in this thesis outperform state of the art quantitative and qualitative methods of analysis

Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells

© 2014 Tafavogh et al.; licensee BioMed Central Ltd. Background: Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity; and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points.Results: We compare the cell counting results of our system with those of a pathologist as the ground-truth. We also compare the system with three state-of-the-art methods, and the results of statistical tests show a significant improvement in the performance of our system compared to state-of-the-art methods. The F-measure obtained by our system is 88.70%. To evaluate the generalizability of our algorithm, we apply it to images of follicular lymphoma, which has similar histological regions to NT. Of the algorithms tested, our algorithm obtains the highest F-measure of 92.79%.Conclusion: We develop a novel overlapping cell splitting algorithm to enhance the cellular quantitative analysis of infant neuroblastoma. The performance of the proposed algorithm promises a reliable automated cell counting system for pathology laboratories. Moreover, the high performance obtained by our algorithm for images of follicular lymphoma demonstrates the generalization of the proposed algorithm for cancers with similar histological regions and histological structures

Can Archival Tissue Reveal Answers to Modern Research Questions?: Computer-Aided Histological Assessment of Neuroblastoma Tumours Collected over 60 Years.

Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain consistent over time, could prove useful to research of rare disease types. We applied immunohistochemistry to investigate whether long term storage caused any changes to antigens used diagnostically for neuroblastoma. We constructed and quantitatively assessed a tissue microarray containing neuroblastoma archival material dating between 1950 and 2007. A total of 119 neuroblastoma tissue cores were included spanning 6 decades. Fourteen antibodies were screened across the tissue microarray (TMA). These included seven positive neuroblastoma diagnosis markers (NB84, Chromogranin A, NSE, Ki-67, INI1, Neurofilament Protein, Synaptophysin), two anticipated to be negative (S100A, CD99), and five research antibodies (IL-7, IL-7R, JAK1, JAK3, STAT5). The staining of these antibodies was evaluated using Aperio ImageScope software along with novel pattern recognition and quantification algorithms. This analysis demonstrated that marker signal intensity did not decrease over time and that storage for 60 years had little effect on antigenicity. The construction and assessment of this neuroblastoma TMA has demonstrated the feasibility of using archival samples for research

Different approaches of bibliometric analysis for data analytics applications in non-profit organisations

Aim: Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. Methods: We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. Results: The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times; (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving; (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector; however, it is still at an immature stage, and more efforts are needed to nourish its development. Conclusion: The research findings can leverage future research and add value to the existing literature on the subject of data analytics.</jats:p

Determining cellularity status of tumors based on histopathology using hybrid image segmentation

A Computer Aided Diagnosis (CAD) system is developed to determine cellularity status of a tumor. The system helps pathologists to distinguish a tumor with cell proliferation from normal tumors. The developed CAD system implements a hybrid segmentation method to identify and extract the morphological features that are used by pathologists for determining cellularity status of tumor. Adaptive Mean Shift (AMS) clustering as a non-parametric technique is integrated with Color Template Matching (CTM) to construct segmentation approach. We used Expectation Maximization (EM) clustering as a parametric technique for the sake of comparison with our proposed approach. The output of our proposed system and EM are validated by two pathologists as ground truth. The result of our developed system is quite close to the decision of pathologists, and it significantly outperforms EM in terms of accuracy. © 2012 IEEE

Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images

Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %. © 2013 International Federation for Medical and Biological Engineering

Segmenting neuroblastoma tumor images and splitting overlapping cells using shortest paths between cell contour convex regions

Neuroblastoma is one of the most fatal paediatric cancers. One of the major prognostic factors for neuroblastoma tumour is the total number of neuroblastic cells. In this paper, we develop a fully automated system for counting the total number of neuroblastic cells within the images derived from Hematoxylin and Eosin stained histological slides by considering the overlapping cells. We finally propose a novel multi-stage cell counting algorithm, in which cellular regions are extracted using an adaptive thresholding technique. Overlapping and single cells are discriminated using morphological differences. We propose a novel cell splitting algorithm to split overlapping cells into single cells using the shortest path between contours of convex regions. © 2013 Springer-Verlag

Feature prioritisation on big genomic data for analysing gene-gene interactions

Complex diseases are not caused by single genes but result from intricate non-linear interactions among them. There is a critical need to implement approaches that take into account non-linear gene-gene interactions in searching for markers that jointly cause diseases. Determining the interaction between more than two single nucleotide polymorphisms (SNP) within the whole genome data is computationally expensive and often infeasible. In this paper, we develop an approach to classify patients with Acute Lymphoblastic Leukaemia by analysing multiple SNP interactions. A novel feature prioritisation algorithm called interaction effect quantity (IEQ) selects SNPs with high potential of interaction by analysing their distribution throughout the genomic data and enables deeper analysis of non-linear interactions within large datasets. We show that IEQ enables analyses of interactions between up to four SNPs, with F-measure for classification greater than 89% obtained. Such an analysis is typically much more computationally challenging if IEQ is not implemented