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Found 6 papers, 0 papers with code
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.
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.
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.
