27 citations · 30 across the 19 of their papers we have counts for
21 papers
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…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariance
Lin Tian, Xiaoling Hu, Juan Eugenio Iglesias
Accurate image registration is essential in many medical imaging applications, yet most deep registration networks provide little indication of when or where their predictions are…
A Modality-agnostic Multi-task Foundation Model for Human Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +5
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…
Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology
Jonathan Williams Ramirez, Dina Zemlyanker, Lucas Deden-Binder +15
Advances in image registration and machine learning have recently enabled volumetric analysis of postmortem brain tissue from conventional photographs of coronal slabs, which are r…
UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
Raghav Mehta, Karthik Gopinath, Ben Glocker +1
We propose UNSURF, a novel uncertainty measure for cortical surface reconstruction of clinical brain MRI scans of any orientation, resolution, and contrast. It relies on the discre…
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…
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Stefano Cerri, Asbjørn Munk, Sebastian Nørgaard Llambias +11
We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910…
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…
Learning to Upscale 3D Segmentations in Neuroimaging
Xiaoling Hu, Peirong Liu, Dina Zemlyanker +3
Obtaining high-resolution (HR) segmentations from coarse annotations is a pervasive challenge in computer vision. Applications include inferring pixel-level segmentations from toke…
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…
Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization
Peirong Liu, Ana Lawry Aguila, Juan E. Iglesias
Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular reso…
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…
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +2
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari +70
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing gene…