22 citations · 75 across the 6 of their papers we have counts for
6 papers
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons +2
Clinical diagnosis of the pediatric musculoskeletal system relies on the analysis of medical imaging examinations. In the medical image processing pipeline, semantic segmentation u…
Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning
Arnaud Boutillon, Asma Salhi, Valérie Burdin +1
Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape represen…
Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets
Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons +2
Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice. However, the accuracy…
Multi-Structure Deep Segmentation with Shape Priors and Latent Adversarial Regularization
Arnaud Boutillon, Bhushan Borotikar, Christelle Pons +2
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
Arnaud Boutillon, Bhushan Borotikar, Valérie Burdin +1
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatr…
Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
Arnaud Boutillon, Bhushan Borotikar, Valérie Burdin +1
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning. The purpose of this work is to incorporate anatomical…