Quantifying uncertainty in deep learning of radiologic images
In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …
[HTML][HTML] Atlas-ISTN: joint segmentation, registration and atlas construction with image-and-spatial transformer networks
Deep learning models for semantic segmentation are able to learn powerful representations
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …
Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics
Abstract Purpose of Review Musculoskeletal imaging serves a critical role in clinical care
and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in …
and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in …
From images to probabilistic anatomical shapes: A deep variational bottleneck approach
J Adams, S Elhabian - … Conference on Medical Image Computing and …, 2022 - Springer
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for
detecting pathology, diagnosing disease, and conducting population-level morphology …
detecting pathology, diagnosing disease, and conducting population-level morphology …
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 …
An efficient muscle segmentation method via bayesian fusion of probabilistic shape modeling and deep edge detection
Objective: Paraspinal muscle segmentation and reconstruction from MR images are critical
to implement quantitative assessment of chronic and recurrent low back pains. Due to …
to implement quantitative assessment of chronic and recurrent low back pains. Due to …
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 …
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 …