Search Results for author:
Found 7 papers, 3 papers with code
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology.
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
The main contributions of our work are: (1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; (2) a deep representation learning detection mechanism, \emph{ensemble space learning}, for robust object localization; and (3) \emph{marginal shape deep learning} for the shape deformation parameter estimation.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.
Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation
In this work, we propose a partitioned joint statistical shape model approach with sparse appearance learning for the segmentation of healthy and pathological AVP.
