| dc.date.accessioned | 2023-09-15T06:21:13Z | |
| dc.date.available | 2023-09-15T06:21:13Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://hdl.handle.net/10852/105010 | |
| dc.description.abstract | Echocardiography, i.e. ultrasound imaging of the heart, is the most frequently used modality and to be able to save clinicians’ time when analyzing such exams, automatic algorithms are being developed mainly using Deep Learning techniques. However, privacy concerns, limited data availability and variability in echocardiography images pose significant challenges for developing such models. Therefore, this thesis described the developed and applied deep generative models that can efficiently and accurately generate synthetic 2D and 3D echocardiography images, with a high realism level. Results showed that the synthetic images are realistic and are a helpful and relevant resource which can be used to develop the Deep Learning algorithms for echocardiography image analysis, this way facilitating clinical workflows, as they represent a promising step towards more efficient and effective medical imaging and diagnosis. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.haspart | Paper I: Tiago, C., Gilbert, A., Salem Beela, A., Aase, S.A., Snare, S.R., Sprem, J., and McLeod, K. “A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images Using a GAN”. In: IEEE Access. Vol. 10, 2022, pp. 98803–98815. DOI: 10.1109/ACCESS.2022.3207177. The accepted version is included in the thesis. Also available at: https://doi.org/10.1109/ACCESS.2022.3207177 | |
| dc.relation.haspart | Paper II: Tiago, C., Snare, S.R., Sprem, J., and McLeod, K. “A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images”. In: IEEE Access. Vol. 11, 2023, pp. 17594–17602. DOI: 10.1109/ACCESS.2023.3246762. The accepted version is included in the thesis. Also available at: https://doi.org/10.1109/ACCESS.2023.3246762 | |
| dc.relation.haspart | Paper III: Tiago, C., Snare, S.R., McLeod, K., and Sprem, J.. “Denoising Diffusion Model for 3D Echocardiography Image Generation: Image Usability and Clinical Relevance”. Submitted for publication to IEEE Open Journal of Engineering in Medicine and Biology. The paper is not available in DUO awaiting publishing. | |
| dc.relation.uri | https://doi.org/10.1109/ACCESS.2022.3207177 | |
| dc.relation.uri | https://doi.org/10.1109/ACCESS.2023.3246762 | |
| dc.title | Deep Generative Models Applied to 2D and 3D Echocardiography: Image Generation and Analysis | en_US |
| dc.type | Doctoral thesis | en_US |
| dc.creator.author | Tiago, Cristiana Ferreira | |
| dc.type.document | Doktoravhandling | en_US |