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Full-Text Articles in Engineering
Cross-Correlation Template Matching For Liver Localisation In Computed Tomography, Patrick Leyden, Martin O'Connell, Derek Greene, Kathleen Curran
Cross-Correlation Template Matching For Liver Localisation In Computed Tomography, Patrick Leyden, Martin O'Connell, Derek Greene, Kathleen Curran
Session 5: Medical and Biomedical Imaging
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. Cross- Correlation, 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 …
Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks For Healthy Bone Marrow Baseline Image Generation, Patrick Leydon, Martin O'Connell, Derek Greene, Kathleen Curran
Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks For Healthy Bone Marrow Baseline Image Generation, Patrick Leydon, Martin O'Connell, Derek Greene, Kathleen Curran
Session 6: Applications, Architecture and Systems Integration
A Conditional-Generative Adversarial Network has been used for a supervised image-to-image transla- tion 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.
Visualizing And Interpreting Feature Reuse Of Pretrained Cnns For Histopathology, Mara Graziani, Vincent Andrearczyk, Henning Muller
Visualizing And Interpreting Feature Reuse Of Pretrained Cnns For Histopathology, Mara Graziani, Vincent Andrearczyk, Henning Muller
Session 6: Applications, Architecture and Systems Integration
Reusing the parameters of networks pretrained on large scale datasets of natural images, such as ImageNet, is a common technique in the medical imaging domain. The large variability of objects and classes is, however, drastically reduced in most medical applications where images are dominated by repetitive patterns with, at times, subtle differences between the classes. This paper takes the example of finetuning a pretrained convolutional network on a histopathology task. Because of the reduced visual variability in this application domain, the network mostly learns to detect textures and simple patterns. As a result, the complex structures that maximize the channel …