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Found 7 papers, 0 papers with code
Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
We propose an extension to \particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest.
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
To address this, we propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment.
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).
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population.
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved.
