713 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS20 papers
- Massachusetts General HospitalUS16 papers
- Otto-von-Guericke-Universität MagdeburgDE13 papers
- Center for Behavioral Brain SciencesDE12 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS11 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
- Technical University of MunichDE7 papers
- University of BonnDE7 papers
- Massachusetts Institute of TechnologyUS5 papers
- Boston Children's HospitalUS4 papers
- Heidelberg UniversityDE4 papers
- Human TechnopoleIT4 papers
57 papers
MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction
Antonio Ortiz-Gonzalez, Erich Kobler, Lukas Schletter +1
Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. E…
ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy
David Bertram, Anja Ophey, Sinah Röttgen +18
Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of -synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia…
On the robustness of the emergent spatiotemporal dynamics in biophysically realistic and phenomenological whole-brain models at multiple network resolutions
Cristiana Dimulescu, Ronja Strömsdörfer, Agnes Flöel +1
The human brain is a complex dynamical system which displays a wide range of macroscopic and mesoscopic patterns of neural activity, whose mechanistic origin remains poorly underst…
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Nancy R. Newlin, Kurt Schilling, Serge Koudoro +33
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (…
Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt
Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari +28
Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutio…
Deep learning empowered sensor fusion boosts infant movement classification
Tomas Kulvicius, Dajie Zhang, Luise Poustka +8
To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments…
Statistical modeling to adjust for time trends in adaptive platform trials utilizing non-concurrent controls
Pavla Krotka, Martin Posch, Mohamed Gewily +2
Utilizing non-concurrent control data (NCC) in the analysis of late-entering arms in platform trials has recently received considerable attention. While incorporating NCC can lead…
Translating the future: Image-to-image translation for the prediction of future brain metabolism
Elena Doering, Merle C. Hönig, Tobias Deußer +4
Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to cognitive decline. [F]-Fluorodeoxyglucose positron emission tomography ([F]-FDG PET) i…
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra +5
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, pau…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
Santiago Estrada, David Kügler, Emad Bahrami +5
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions. However, despite its importance, only a few small-s…
Estimating Head Motion from MR-Images
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 perf…
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Soumick Chatterjee, Hana Haseljić, Robert Frysch +9
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm con…
Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Soumick Chatterjee, Pavan Tummala, Oliver Speck +1
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued d…
An automated, geometry-based method for hippocampal shape and thickness analysis
Kersten Diers, Hannah Baumeister, Frank Jessen +3
The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and…
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
Hana Haseljić, Soumick Chatterjee, Robert Frysch +9
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT…
Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions
Leonie Henschel, David Kügler, Derek S Andrews +2
Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper,…
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
Soumick Chatterjee, Himanshi Bajaj, Istiyak H. Siddiquee +5
Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote…
FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI
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. Yet, only few, time-intensive automated imag…
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski +8
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 ass…
High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced SSFP frequency profile
Florian Birk, Felix Glang, Alexander Loktyushin +4
We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimat…
Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
Santiago Estrada, Ran Lu, Kersten Diers +5
The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic proce…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger +1
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and…