2 citations · 3 across the 12 of their papers we have counts for
14 papers
SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images
Krithika Iyer, Jadie Adams, Shireen Y. Elhabian
Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements i…
Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds
Jadie Adams, Shireen Elhabian
Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterizat…
Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images
Jadie Adams, Krithika Iyer, Shireen Elhabian
Anatomical shape analysis plays a pivotal role in clinical research and hypothesis testing, where the relationship between form and function is paramount. Correspondence-based stat…
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
Rachaell Nihalaani, Tushar Kataria, Jadie Adams +1
Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing in…
Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models
Abu Zahid Bin Aziz, Jadie Adams, Shireen Elhabian
Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these application…
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation
Jadie Adams, Shireen Y. Elhabian
Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning. However, quantifying and understanding the uncertainty a…