no code implementations • 25 Apr 2024 • Karthik Gopinath, Xiaoling Hu, Malte Hoffmann, Oula Puonti, Juan Eugenio Iglesias
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects.
no code implementations • 26 Feb 2024 • William Kelley, Nathan Ngo, Adrian V. Dalca, Bruce Fischl, Lilla Zöllei, Malte Hoffmann
With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.
1 code implementation • 21 Dec 2023 • Benjamin Billot, Neel Dey, Daniel Moyer, Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Ellen Grant, Polina Golland
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
no code implementations • 26 Jan 2023 • Malte Hoffmann, Andrew Hoopes, Douglas N. Greve, Bruce Fischl, Adrian V. Dalca
Most affine methods are agnostic to anatomy, meaning the registration will be inaccurate if algorithms consider all structures in the image.
1 code implementation • 25 Jan 2023 • Nalini M. Singh, Neel Dey, Malte Hoffmann, Bruce Fischl, Elfar Adalsteinsson, Robert Frost, Adrian V. Dalca, Polina Golland
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
no code implementations • 30 Mar 2022 • Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John Guttag, Adrian V. Dalca
We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values.
no code implementations • 18 Mar 2022 • Andrew Hoopes, Jocelyn S. Mora, Adrian V. Dalca, Bruce Fischl, Malte Hoffmann
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
no code implementations • 8 Oct 2021 • Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Leah Morgan, Paul Wighton, M. Dylan Tisdall, Martin Reuter, Elfar Adalsteinsson, P. Ellen Grant, Lawrence L. Wald, André J. W. van der Kouwe
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment.
1 code implementation • 4 Jan 2021 • Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian V. Dalca
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training.
no code implementations • 21 Apr 2020 • Malte Hoffmann, Benjamin Billot, Douglas N. Greve, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
no code implementations • 17 Jul 2018 • Michaela Regneri, Malte Hoffmann, Jurij Kost, Niklas Pietsch, Timo Schulz, Sabine Stamm
Predictive geometric models deliver excellent results for many Machine Learning use cases.