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Found 10 papers, 1 papers with code
Fully automated workflow for the design of patient-specific orthopaedic implants: application to total knee arthroplasty
The workflow accuracy was $0. 4\pm0. 2mm$ for the segmentation, $1. 2\pm0. 4mm$ for the full bones reconstruction, and $2. 8\pm2. 2mm$ for the anatomical landmarks determination.
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations.
Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning
This mapping is learned from a synthetic population generated by the standard SSM.
Dynamic multi feature-class Gaussian process models
A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose).
Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets
Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice.
Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization
Automatic segmentation of the musculoskeletal system in pediatric magnetic resonance (MR) images is a challenging but crucial task for morphological evaluation in clinical practice.
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice.
Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints
In this paper, we propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints in a continuous domain.
Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning.
Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders
Methodological aspects are evaluated in a leave-one-out fashion on a dataset of 24 shoulder examinations from patients with obstetrical brachial plexus palsy and focus on 4 different muscles including deltoid as well as infraspinatus, supraspinatus and subscapularis from the rotator cuff.
