641 citations · 823 across the 21 of their papers we have counts for
12 papers · 1 filter
Uncertainty-Aware Cortical Surface Reconstruction and Spherical Registration for Heterogeneous Brain MRI
Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes +4
Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, allowing quantities such as curva…
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Ana Lawry Aguila, Peirong Liu, Oula Puonti +1
Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scena…
End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images
Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes +7
Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) thickness estimation. However, mo…
Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinates
Lin Tian, Jonathan Williams-Ramirez, Dina Zemlyanker +14
Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to in vivo scans. There is increasing interest in building these correlatio…
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…
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
Xiaoling Hu, Annabel Sorby-Adams, Frederik Barkhof +3
White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis. Automated WMH segmentation methods enable quantitative analysis via estimation…
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…
H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation
Livia Rodrigues, Martina Bocchetta, Oula Puonti +6
The hypothalamus is a small structure located in the center of the brain and is involved in significant functions such as sleeping, temperature, and appetite control. Various neuro…
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Pablo Laso, Stefano Cerri, Annabel Sorby-Adams +21
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated se…
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Benjamin Billot, Douglas N. Greve, Oula Puonti +5
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are hi…
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Oula Puonti, Dominik S. Meier +4
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple s…