Deep learning techniques to bridge the gap between 2D and 3D ultrasound imaging - Yeung, PH (2022)
Three-dimensional (3D) ultrasound imaging has contributed to our understanding of fetal developmental processes in the womb by providing rich contextual information of the inherently 3D anatomies. However, its use is limited in clinical settings, due to the high purchasing costs and limited diagnostic practicality. Freehand two-dimensional (2D) ultrasound imaging, in contrast, is routinely used in standard obstetric exams. The low cost and portability of 2D ultrasound render it uniquely suitable for use in low- and middle-income settings. However, high level of expertise is always involved and it inherently lacks a 3D representation of the anatomies, which limit its potential for more accessible and advanced assessment. Capitalizing on the flexibility offered by freehand 2D ultrasound acquisition, this thesis presents a deep learning-based framework for optimizing the utilization and diagnostic power of 2D freehand ultrasound in fetal brain imaging.