Now showing 1 - 3 of 3
  • Publication
    Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks for Healthy Bone Marrow Baseline Image Generation
    A Conditional-Generative Adversarial Network has been used for a supervised image-to-image translation task which outputs a synthetic PET scan based on real patient CT data. The network is trained using only data of patients with healthy bone marrow metabolism. This allows for a patient specific synthetic healthy baseline scan to be produced. This can be used by a clinician for comparison to real PET data in the absence of a baseline scan or to aid in the diagnosis of conditions such as Multiple Myeloma which manifest as changes in bone marrow metabolism
  • Publication
    Cross-Correlation Template Matching for Liver Localisation in Computed Tomography
    Many of the current approaches to automatic organ localisation in medical imaging require a large amount of labelled patient data to train systems to accurately identify specific anatomical features. CrossCorrelation, also known as template matching, is a statistical method of assessing the similarity between a template image and a target image. This method has been modified and presented here to localize the liver in Computed Tomography volume images in the Coronal and Sagital planes to achieve a mean positioning error of approximately 11 mm and 20 mm respectively based on between 1 and 25 datasets to create the template liver.
  • Publication
    Machine Learning in Prediction of Prostate Brachytherapy Rectal Dose Classes at Day 30
    (Irish Pattern Recognition & Classification Society, 2015-08-28) ; ; ; ; ;
    A retrospective analysis of brachytherapy implant data was carried out on 351 patients that underwent permanent I125 brachytherapy for treatment of low-risk prostate cancer. For each patient, the dose received by 2cm3 of the rectum (D2cc) 30 days post implant was defined as belonging one of two classes, ”Low” and ”High” depending on whether or not it was above or below a particular dose threshold. The aim of the study was to investigate the application of a number of machine learning classification techniques to intra-operative implant dosimetry data for prediction of rectal dose classes determined 30 days post implant. Algorithm performance was assessed in terms of its true and false positive rates and Receiver Operator Curve area based on a 10-fold cross validation procedure using Weka software. This was repeated for a variety of dose class thresholds to determine the point at which the highest accuracy was achieved. The highest ROC areas were observed at a threshold of D2cc = 90 Gy, with the highest area achieved by Bayes Net (0.943). At more clinically useful thresholds of D2cc = 145 Gy, classification was less reliable, with the highest ROC area achieved by Bayes Net (0.613).