27 citations · 30 across the 19 of their papers we have counts for
7 papers · 1 filter
Learn2Synth: Learning Optimal Data Synthesis Using Hypergradients for Brain Image Segmentation
Xiaoling Hu, Xiangrui Zeng, Oula Puonti +3
Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Randomization allows networks to s…
Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
Xiaoling Hu, Karthik Gopinath, Peirong Liu +4
Over recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human neuroimaging with mag…
Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI
Karthik Gopinath, Douglas N. Greve, Colin Magdamo +4
Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditi…
High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models
Livia Rodrigues, Martina Bocchetta, Oula Puonti +6
Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or sh…
Registration by Regression (RbR): a framework for interpretable and flexible atlas registration
Karthik Gopinath, Xiaoling Hu, Malte Hoffmann +2
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects. Machine learn…
PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Peirong Liu, Oula Puonti, Annabel Sorby-Adams +2
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR p…
JUMP: A joint multimodal registration pipeline for neuroimaging with minimal preprocessing
Adria Casamitjana, Juan Eugenio Iglesias, Raul Tudela +2
We present a pipeline for unbiased and robust multimodal registration of neuroimaging modalities with minimal pre-processing. While typical multimodal studies need to use multiple…