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Leydon, Patrick
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Leydon, Patrick
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Leydon, Patrick
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Now showing 1 - 2 of 2
- PublicationCross-Correlation Template Matching for Liver Localisation in Computed Tomography(2019-07-30)
; ; ; 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.190 - PublicationSynthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks for Healthy Bone Marrow Baseline Image Generation(2019-08-30)
; ; ; 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 metabolism290