Fully bayesian vib-deepssm
J Adams, SY Elhabian - … Conference on Medical Image Computing and …, 2023 - Springer
Statistical shape modeling (SSM) enables population-based quantitative analysis of
anatomical shapes, informing clinical diagnosis. Deep learning approaches predict …
anatomical shapes, informing clinical diagnosis. Deep learning approaches predict …
Scorp: statistics-informed dense correspondence prediction directly from unsegmented medical images
Statistical shape modeling (SSM) is a powerful computational framework for quantifying and
analyzing the geometric variability of anatomical structures, facilitating advancements in …
analyzing the geometric variability of anatomical structures, facilitating advancements in …
S3M: scalable statistical shape modeling through unsupervised correspondences
Statistical shape models (SSMs) are an established way to represent the anatomy of a
population with various clinically relevant applications. However, they typically require …
population with various clinically relevant applications. However, they typically require …
Can point cloud networks learn statistical shape models of anatomies?
J Adams, SY Elhabian - … Conference on Medical Image Computing and …, 2023 - Springer
Abstract Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying
anatomical variations within populations of anatomies. However, traditional correspondence …
anatomical variations within populations of anatomies. However, traditional correspondence …
Mesh2ssm: From surface meshes to statistical shape models of anatomy
K Iyer, SY Elhabian - … Conference on Medical Image Computing and …, 2023 - Springer
Statistical shape modeling is the computational process of discovering significant shape
parameters from segmented anatomies captured by medical images (such as MRI and CT …
parameters from segmented anatomies captured by medical images (such as MRI and CT …
Weakly supervised Bayesian shape modeling from unsegmented medical images
Anatomical shape analysis is pivotal in clinical research and hypothesis testing, where the
relationship between form and function is paramount. Correspondence-based statistical …
relationship between form and function is paramount. Correspondence-based statistical …
Point2ssm: Learning morphological variations of anatomies from point cloud
J Adams, S Elhabian - arXiv preprint arXiv:2305.14486, 2023 - arxiv.org
We introduce Point2SSM, a novel unsupervised learning approach that can accurately
construct correspondence-based statistical shape models (SSMs) of anatomy directly from …
construct correspondence-based statistical shape models (SSMs) of anatomy directly from …
[PDF][PDF] Localization-aware deep learning framework for statistical shape modeling directly from images
J Ukey, S Elhabian - Medical Imaging with Deep Learning, 2023 - sci.utah.edu
Abstract Statistical Shape Modelling (SSM) is an effective tool for quantitatively analyzing
anatomical populations. SSM has benefitted largely from advances in deep learning where …
anatomical populations. SSM has benefitted largely from advances in deep learning where …
A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning
We introduce a novel unsupervised deep learning framework for constructing statistical
shape models (SSMs). Although unsupervised learning-based 3D shape matching methods …
shape models (SSMs). Although unsupervised learning-based 3D shape matching methods …
Image2SSM: Reimagining statistical shape models from images with radial basis functions
H Xu, SY Elhabian - … Conference on Medical Image Computing and …, 2023 - Springer
Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical
morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation …
morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation …