no code implementations • 29 Nov 2023 • Leonie Henschel, David Kügler, Lilla Zöllei, Martin Reuter
However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation.
1 code implementation • 24 Aug 2023 • Santiago Estrada, David Kügler, Emad Bahrami, Peng Xu, Dilshad Mousa, Monique M. B. Breteler, N. Ahmad Aziz, Martin Reuter
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions.
1 code implementation • 28 Feb 2023 • Clemens Pollak, David Kügler, Martin Reuter
Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed.
1 code implementation • 1 Feb 2023 • Kersten Diers, Hannah Baumeister, Frank Jessen, Emrah Düzel, David Berron, Martin Reuter
In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature.
1 code implementation • 29 Jun 2022 • Leonie Henschel, David Kügler, Derek S Andrews, Christine W Nordahl, Martin Reuter
We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions.
no code implementations • 17 Dec 2021 • Leonie Henschel, David Kügler, Martin Reuter
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1. 0 mm for improved structure definition and morphometry.
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 • 9 Aug 2021 • Santiago Estrada, Ran Lu, Kersten Diers, Weiyi Zeng, Philipp Ehses, Tony Stöcker, Monique M. B Breteler, Martin Reuter
The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function.
no code implementations • 9 Sep 2020 • Christian Ewert, David Kügler, Anastasia Yendiki, Martin Reuter
Here, we introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images, removing the dependency of conventional segmentation methods on T 1-weighted images and slow pre-processing pipelines.
1 code implementation • 9 Oct 2019 • Leonie Henschel, Sailesh Conjeti, Santiago Estrada, Kersten Diers, Bruce Fischl, Martin Reuter
In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation.
1 code implementation • 3 Apr 2019 • Santiago Estrada, Ran Lu, Sailesh Conjeti, Ximena Orozco-Ruiz, Joana Panos-Willuhn, Monique M. B Breteler, Martin Reuter
Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the Rhineland Study - a large prospective population-based study.
no code implementations • 20 Jul 2018 • Santiago Estrada, Sailesh Conjeti, Muneer Ahmad, Nassir Navab, Martin Reuter
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation.
no code implementations • 9 Jul 2018 • Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter
Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI).
no code implementations • 27 Feb 2017 • Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images.