2 citations · 3 across the 6 of their papers we have counts for
6 papers
Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods
Ghazal Danaee, Marc Niethammer, Jarrett Rushmore +1
Deep-learning-based segmentation algorithms have substantially advanced the field of medical image analysis, particularly in structural delineations in MRIs. However, an important…
Estimation of Head Motion in Structural MRI and its Impact on Cortical Thickness Measurements in Retrospective Data
Charles Bricout, Samira Ebrahimi Kahou, Sylvain Bouix
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. These biases can interfere wi…
Improving Quality Control Of MRI Images Using Synthetic Motion Data
Charles Bricout, Kang Ik K. Cho, Michael Harms +10
MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To add…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…
uniGradICON: A Foundation Model for Medical Image Registration
Lin Tian, Hastings Greer, Roland Kwitt +5
Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generi…
: Approximate Diffeomorphisms via Gradient Inverse Consistency
Lin Tian, Hastings Greer, François-Xavier Vialard +6
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration tec…