Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Adams, Jadie"'
Autor:
Adams, Jadie, Elhabian, Shireen
Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterization and quantification of anatomical shapes such as bones and organs,
Externí odkaz:
http://arxiv.org/abs/2405.09707
Anatomical shape analysis plays a pivotal role in clinical research and hypothesis testing, where the relationship between form and function is paramount. Correspondence-based statistical shape modeling (SSM) facilitates population-level morphometric
Externí odkaz:
http://arxiv.org/abs/2405.09697
Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional m
Externí odkaz:
http://arxiv.org/abs/2404.17967
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 interest in self-supervised approaches in tandem with the abundance of a
Externí odkaz:
http://arxiv.org/abs/2403.12290
Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved
Externí odkaz:
http://arxiv.org/abs/2310.01529
Autor:
Adams, Jadie, Elhabian, Shireen Y.
Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning. However, quantifying and understanding the uncertainty associated with model predictions is crucial in critical clinical appli
Externí odkaz:
http://arxiv.org/abs/2308.07506
Autor:
Adams, Jadie, Elhabian, Shireen
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 clinical research, enabling population-level analysis of morphologica
Externí odkaz:
http://arxiv.org/abs/2305.14486
Autor:
Adams, Jadie, Elhabian, Shireen
Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated u
Externí odkaz:
http://arxiv.org/abs/2305.05797
Autor:
Adams, Jadie, Elhabian, Shireen
Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and req
Externí odkaz:
http://arxiv.org/abs/2305.05610
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 foreground emissions, obscuring the CMB signal and reducing its efficacy i
Externí odkaz:
http://arxiv.org/abs/2302.12378