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Focused shape models for hip joint segmentation in 3D magnetic resonance images.

, , , , , and . Medical Image Anal., 18 (3): 567-578 (2014)

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Research and applications: MilxXplore: a web-based system to explore large imaging datasets., , , , , and . JAMIA, 20 (6): 1046-1052 (2013)Focused shape models for hip joint segmentation in 3D magnetic resonance images., , , , , and . Medical Image Anal., 18 (3): 567-578 (2014)Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan., , , , , , , , , and 9 other author(s). NeuroImage, (2020)Early brain morphometrics from neonatal MRI predict motor and cognitive outcomes at 2-years corrected age in very preterm infants., , , , , , , , , and 5 other author(s). NeuroImage, (2023)Brain microstructure and morphology of very preterm-born infants at term equivalent age: Associations with motor and cognitive outcomes at 1 and 2 years., , , , , and . NeuroImage, (2020)Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative., , , , , and . CoRR, (2022)Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression, , , , , and . NeuroImage: Clinical, (2014)Automated Plant and Leaf Separation: Application in 3D Meshes of Wheat Plants., , , , , , , and . DICTA, page 1-7. IEEE, (2016)Anomaly-Aware 3D Segmentation of Knee Magnetic Resonance Images., , , , and . MIDL, volume 172 of Proceedings of Machine Learning Research, page 1360-1374. PMLR, (2022)Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach., , , , , , and . GLMI@MICCAI, volume 11849 of Lecture Notes in Computer Science, page 96-103. Springer, (2019)