Automatic cartilage segmentation in magnetic resonance imaging. Osteoarthritis (OA) is the most common form of arthritis, affecting nearly 1.4 million Australians. This research aims at engineering new tools for use in Magnetic Resonance Imaging systems to enable automated analyses of the cartilage and bones in joint images. The goals of the work are to assist with improved diagnosis and treatment planning for both chronic disease, such as OA, and acute injuries, such as cartilage and ligament ....Automatic cartilage segmentation in magnetic resonance imaging. Osteoarthritis (OA) is the most common form of arthritis, affecting nearly 1.4 million Australians. This research aims at engineering new tools for use in Magnetic Resonance Imaging systems to enable automated analyses of the cartilage and bones in joint images. The goals of the work are to assist with improved diagnosis and treatment planning for both chronic disease, such as OA, and acute injuries, such as cartilage and ligament tears in sporting injuries and other traumas.
The software developed will be provided on the project’s partner (Siemens) platform and will therefore be available worldwide and have a consequently large impact on the field.
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Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earl ....Quantitative measurement of Schizophrenia using Electrovestibulography. Schizophrenia was estimated to cost approximately $1.85billion in 2001 (0.3% of GDP and nearly $50k for each of the 37,000 Australians with the illness). Over one third of the cost is borne by sufferers and their carers. Misdiagnosis and incorrect therapy are common. To date quantitative assessment of Schizophrenics has been impossible making this tool potentially invaluable. An accurate diagnostic test could facilitate earlier diagnosis, more accurate treatment plans, and prevention of debilitating psychotic episodes for the sufferer. By being able to monitor drug efficacy the community can benefit by reduced drug costs, confinement times and hastened new drug development. Read moreRead less
Modelling stock market liquidity in Australia and the Asia Pacific Region. This project will develop new methods of assessing stock market liquidity in Australia and the Asia-Pacific region. These methods will use high frequency transactions-based data provided by the industry partner, SIRCA. The data will be the basis of smart information real time algorithms for measuring market liquidity. They will incorporate generalizations and extensions of recent developments in time series econometrics, ....Modelling stock market liquidity in Australia and the Asia Pacific Region. This project will develop new methods of assessing stock market liquidity in Australia and the Asia-Pacific region. These methods will use high frequency transactions-based data provided by the industry partner, SIRCA. The data will be the basis of smart information real time algorithms for measuring market liquidity. They will incorporate generalizations and extensions of recent developments in time series econometrics, and will be calibrated and evaluated statistically. The novel methods will be crucial to market participants and to regulators, who will be able to apply them to assess market depth and liquidity, and reduce trading costs substantially.Read moreRead less
Learning Medical Image Knowledge. We aim to develop Machine Learning and Knowledge Acquisition techniques for automated recognition of features in medical images, and to provide decision support for diagnosis from medical images. The project is innovative in its use of layered learning, where the computer first learns to recognise low-level image features that are then used to learn more complex features. The project is also innovative in combining a variety of automatic learning methods, includ ....Learning Medical Image Knowledge. We aim to develop Machine Learning and Knowledge Acquisition techniques for automated recognition of features in medical images, and to provide decision support for diagnosis from medical images. The project is innovative in its use of layered learning, where the computer first learns to recognise low-level image features that are then used to learn more complex features. The project is also innovative in combining a variety of automatic learning methods, including relational learning, with human-assisted knowledge acquisition methods. The expected outcomes will be new techniques for image understanding, particularly for our test domain, namely, High Resolution Computed Tomography scans of the lung.Read moreRead less
Haemodynamic investigation of flow diverter stents for the treatment of intracranial aneurysms. This project will explore the engineering of a flow diverter, an endovascular device for the treatment of brain aneurysms. The project will determine the optimal design of new types of flow diverters, which in turn could improve the effectiveness of treatments, thus reducing the associated costs of cerebral haemorrhage and stroke.
X-ray Micro-tomography Validation of HRCT-Based Airway Measurements. This project brings together a newly emergent modality of microscopy in the form of 3D X-ray micro-tomography (XRMT) along with leading-edge image analysis to develop breakthrough science in respiratory research aimed at improving the reliability of high resolution computed tomography (HRCT). The project will develop novel 3D lung image segmentation protocols, a stereotactic registration program allowing 3D matching of XRCT and ....X-ray Micro-tomography Validation of HRCT-Based Airway Measurements. This project brings together a newly emergent modality of microscopy in the form of 3D X-ray micro-tomography (XRMT) along with leading-edge image analysis to develop breakthrough science in respiratory research aimed at improving the reliability of high resolution computed tomography (HRCT). The project will develop novel 3D lung image segmentation protocols, a stereotactic registration program allowing 3D matching of XRCT and HRCT data sets, and a validation protocol for quantitative HRCT analysis of airway disease. These outcomes will allow wider application of HRCT to non-invasively follow the dynamics of pulmonary function.Read moreRead less
The importance of price and perceived quality to the demand for fresh fruits and vegetables. It is estimated that the direct and indirect cost of diet-related diseases to Australia is between $2-3 billion per annum. One of the most important things that can be done to prevent diet-related disease is to encourage the population to eat more fruits and vegetables. The aim of this project is to examine the effect of price and perceived quality on the type and quantity of fruits and vegetables cons ....The importance of price and perceived quality to the demand for fresh fruits and vegetables. It is estimated that the direct and indirect cost of diet-related diseases to Australia is between $2-3 billion per annum. One of the most important things that can be done to prevent diet-related disease is to encourage the population to eat more fruits and vegetables. The aim of this project is to examine the effect of price and perceived quality on the type and quantity of fruits and vegetables consumed, especially among low-income households. The project will employ modelling techniques that are novel to this product group and that will provide valuable insights to the constraints and opportunities for increasing fruits and vegetables intake, as well as benchmarks for future research.Read moreRead less
Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less
Automated pathogen detection using time-gated luminescence microscopy. A rapid and general means of in-situ pathogen identification would benefit the community by ensuring that appropriate treatments can be applied in the early stages of a disease. Patient prognosis is thereby improved and opportunities for multi-drug resistant organisms to arise are limited. Time-gated luminescence microscopy (TgM) exploits persistent luminescence to overcome autofluorescence, a serious problem in pathogen dete ....Automated pathogen detection using time-gated luminescence microscopy. A rapid and general means of in-situ pathogen identification would benefit the community by ensuring that appropriate treatments can be applied in the early stages of a disease. Patient prognosis is thereby improved and opportunities for multi-drug resistant organisms to arise are limited. Time-gated luminescence microscopy (TgM) exploits persistent luminescence to overcome autofluorescence, a serious problem in pathogen detection. Drug-resistant 'Golden Staph' (MRSA) will be used as the model organism to evaluate TgM efficacy. Ultimately however, TgM will be applied for the detection of tuberculosis, a highly contagious disease affecting the respiratory system of more than one-third of the world's population.Read moreRead less
Feature Detection and Computer-aided Diagnosis with Longitudinal Tracking of Benign Asbestos-Related Pleural Disease in CT images. This project seeks to develop smart and novel feature extraction and knowledge acquisition techniques to assist radiologists in automated diagnosis and assessment of lung diseases. These outcomes will lead to improved delivery of health services in Australia, including in rural regions. They will lead to more accurate assessment of asbestos related pleural diseases f ....Feature Detection and Computer-aided Diagnosis with Longitudinal Tracking of Benign Asbestos-Related Pleural Disease in CT images. This project seeks to develop smart and novel feature extraction and knowledge acquisition techniques to assist radiologists in automated diagnosis and assessment of lung diseases. These outcomes will lead to improved delivery of health services in Australia, including in rural regions. They will lead to more accurate assessment of asbestos related pleural diseases for compensation and treatment and better followup, leading to earlier treatment and better quality of life for patients suffering from lung diseases. The project will also save costs due to automated assessment as well as the potential for fewer patient scans.Read moreRead less