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…
Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud
Jadie Adams, Shireen Elhabian
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in…
Fully Bayesian VIB-DeepSSM
Jadie Adams, Shireen Elhabian
Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-…
Can point cloud networks learn statistical shape models of anatomies?
Jadie Adams, Shireen Elhabian
Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-bas…
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
Jadie Adams, Steven Lu, Krzysztof M. Gorski +2
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by fore…
Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach
Jadie Adams, Nawazish Khan, Alan Morris +1
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (S…
From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
Jadie Adams, Shireen Elhabian
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology an…
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams +3
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. SSM requires consistent shape representation across sa…
Uncertain-DeepSSM: From Images to Probabilistic Shape Models
Jadie Adams, Riddhish Bhalodia, Shireen Elhabian
Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmenta…