Probabilistic Shape Models of Anatomy Directly from Images
Keywords:Bayesian Deep Learning, Statistical Shape Modeling, Medical Image Processing
AbstractStatistical shape modeling (SSM) is an enabling tool in medical image analysis as it allows for population-based quantitative analysis. The traditional pipeline for landmark-based SSM from images requires painstaking and cost-prohibitive steps. My thesis aims to leverage probabilistic deep learning frameworks to streamline the adoption of SSM in biomedical research and practice. The expected outcomes of this work will be new frameworks for SSM that (1) provide reliable and calibrated uncertainty quantification, (2) are effective given limited or sparsely annotated/incomplete data, and (3) can make predictions from incomplete 4D spatiotemporal data. These efforts will reduce required costs and manual labor for anatomical SSM, helping SSM become a more viable clinical tool and advancing medical practice.
How to Cite
Adams, J. (2023). Probabilistic Shape Models of Anatomy Directly from Images. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16107-16108. https://doi.org/10.1609/aaai.v37i13.26914
AAAI Doctoral Consortium Track