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Found 12 papers, 5 papers with code
SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images
Traditional methods for shape modeling from imaging data demand significant manual and computational resources.
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data.
Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models
The training is performed in multiple scales, and each scale utilizes the output from the previous scale.
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation
Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning.
Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds.
Fully Bayesian VIB-DeepSSM
We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble.
Can point cloud networks learn statistical shape models of anatomies?
Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies.
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe.
Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM).
From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i. e., shape) space.
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images.
Uncertain-DeepSSM: From Images to Probabilistic Shape Models
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 segmentation, shape registration, and the optimization of population-level shape representations.
