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Supporting data for "Data-efficient Ultrasound Imaging Analysis with Deep Learning"
This dataset contains the Supporting data for "Data-efficient Ultrasound Imaging Analysis with Deep Learning". These studies emphasize the importance and demonstrate the feasibility of achieving high deep learning model performance while relaxing data requirements in ultrasound imaging and analysis tasks, including spatial resolution improvement, motion estimation, and tissue segmentation.
Chapter 2 introduces the constrained CycleGAN model, which is a novel approach to generate ultrasound sector images with spatial resolution that is more spatially uniform throughout the entire sector field of view. The used datasets is provided in DataSet (Chapter 2).zip.
Chapter 3 explores the field of 2D (i.e., lateral and axial) motion estimation in ultrasound imaging, with a special focus on the challenging lateral direction. The used datasets is provided in DataSet (Chapter 3).zip.
In Chapter 4, the proposed mask enhanced segmenation model tackles tissue segmentation in dynamic lung ultrasound images. The used datasets is provided in DataSet (Chapter 4).zip.
Funding
Development Of A Novel Cardiac Imaging Technique To Detect Silent Brain Infarction In The Elderly To Prevent Subsequent Vascular Dementia
Innovation and Technology Commission
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