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pro vyhledávání: '"Hoffmann , Malte"'
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 learning registration methods have achieved excellent speed and accuracy bu
Externí odkaz:
http://arxiv.org/abs/2404.16781
Autor:
Kelley, William, Ngo, Nathan, Dalca, Adrian V., Fischl, Bruce, Zöllei, Lilla, Hoffmann, Malte
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts t
Externí odkaz:
http://arxiv.org/abs/2402.16634
Autor:
Billot, Benjamin, Dey, Neel, Moyer, Daniel, Hoffmann, Malte, Turk, Esra Abaci, Gagoski, Borjan, Grant, Ellen, Golland, Polina
Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet,
Externí odkaz:
http://arxiv.org/abs/2312.13534
Publikováno v:
Imaging Neuroscience, 2, 2024, 1-33
Affine image registration is a cornerstone of medical image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an i
Externí odkaz:
http://arxiv.org/abs/2301.11329
Autor:
Singh, Nalini M., Dey, Neel, Hoffmann, Malte, Fischl, Bruce, Adalsteinsson, Elfar, Frost, Robert, Dalca, Adrian V., Golland, Polina
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the
Externí odkaz:
http://arxiv.org/abs/2301.10365
Autor:
Hoopes, Andrew, Hoffmann, Malte, Greve, Douglas N., Fischl, Bruce, Guttag, John, Dalca, Adrian V.
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that a
Externí odkaz:
http://arxiv.org/abs/2203.16680
Publikováno v:
Neuroimage 260, 2022
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. Despite their abundance, popular classical skull-stripping methods are usually tailore
Externí odkaz:
http://arxiv.org/abs/2203.09974
Autor:
Baheti, Bhakti, Chakrabarty, Satrajit, Akbari, Hamed, Bilello, Michel, Wiestler, Benedikt, Schwarting, Julian, Calabrese, Evan, Rudie, Jeffrey, Abidi, Syed, Mousa, Mina, Villanueva-Meyer, Javier, Fields, Brandon K. K., Kofler, Florian, Shinohara, Russell Takeshi, Iglesias, Juan Eugenio, Mok, Tony C. W., Chung, Albert C. S., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolo, Atzori, Manfredo, Muller, Henning, Grobroehmer, Christoph, Siebert, Hanna, Hansen, Lasse, Heinrich, Mattias P., Canalini, Luca, Klein, Jan, Gerken, Annika, Heldmann, Stefan, Hering, Alessa, Hahn, Horst K., Meng, Mingyuan, Bi, Lei, Feng, Dagan, Kim, Jinman, Zeineldin, Ramy A., Karar, Mohamed E., Mathis-Ullrich, Franziska, Burgert, Oliver, Abderezaei, Javid, Pionteck, Aymeric, Chopra, Agamdeep, Kurt, Mehmet, Yan, Kewei, Yan, Yonghong, Tang, Zhe, Ma, Jianqiang, Nasser, Sahar Almahfouz, Kurian, Nikhil Cherian, Meena, Mohit, Shamsi, Saqib, Sethi, Amit, Tustison, Nicholas J., Avants, Brian B., Cook, Philip, Gee, James C., Tian, Lin, Greer, Hastings, Niethammer, Marc, Hoopes, Andrew, Hoffmann, Malte, Dalca, Adrian V., Christodoulidis, Stergios, Estiene, Theo, Vakalopoulou, Maria, Paragios, Nikos, Marcus, Daniel S., Davatzikos, Christos, Sotiras, Aristeidis, Menze, Bjoern, Bakas, Spyridon, Waldmannstetter, Diana
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing general-purpose medical image registration techniques, they have not yet a
Externí odkaz:
http://arxiv.org/abs/2112.06979
Autor:
Hoffmann, Malte, Turk, Esra Abaci, Gagoski, Borjan, Morgan, Leah, Wighton, Paul, Tisdall, M. Dylan, Reuter, Martin, Adalsteinsson, Elfar, Grant, P. Ellen, Wald, Lawrence L., van der Kouwe, André J. W.
Publikováno v:
Int J Imaging Syst Technol, 31 (3), 2021, 1136-1154
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. As motion limits acquisitions to thick slices that preclude r
Externí odkaz:
http://arxiv.org/abs/2110.04140
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical registration methods solve an optimization problem to find a set of spa
Externí odkaz:
http://arxiv.org/abs/2101.01035