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ORCIDNils Daniel Forkert
Nils D. Forkert
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2020 – today
- 2025
[j34]Kimberly Amador
, Noah Pinel, Anthony J. Winder, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata. Medical Image Anal. 99: 103381 (2025)- 2024
- [j33]Alejandro Gutierrez, Kimberly Amador, Anthony J. Winder, Matthias Wilms, Jens Fiehler, Nils D. Forkert:
Annotation-free prediction of treatment-specific tissue outcome from 4D CT perfusion imaging in acute ischemic stroke. Comput. Medical Imaging Graph. 114: 102376 (2024) - [j32]Sarmad Maqsood, Robertas Damasevicius, Sana Shahid, Nils D. Forkert:
MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification. Expert Syst. Appl. 255: 124584 (2024) - [j31]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. Frontiers Artif. Intell. 7 (2024) - [j30]Christopher Nielsen, Raissa Souza, Matthias Wilms, Nils D. Forkert:
Foundation model-driven distributed learning for enhanced retinal age prediction. J. Am. Medical Informatics Assoc. 31(11): 2550-2559 (2024) - [j29]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert:
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. J. Am. Medical Informatics Assoc. 31(11): 2613-2621 (2024) - [j28]Kimberly Amador, Alejandro Gutierrez, Anthony J. Winder, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes. J. Biomed. Informatics 149: 104567 (2024) - [j27]Raissa Souza, Anthony J. Winder, Emma A. M. Stanley, Vibujithan Vigneshwaran, Milton Camacho, Richard Camicioli, Oury Monchi, Matthias Wilms, Nils D. Forkert:
Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? IEEE J. Biomed. Health Informatics 28(4): 2047-2054 (2024) - [c61]Kimberly Amador, Anthony J. Winder, Noah Pinel, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Unveiling the Temporal Patterns of a 4D CTP Stroke Lesion Outcome Prediction Model Through Attention Analysis. ISBI 2024: 1-5 - [c60]Raissa Souza, Emma A. M. Stanley, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? FAIMI/EPIMI@MICCAI 2024: 119-128 - [c59]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Matthias Wilms, G. Bruce Pike, Gabrielle Dagasso, Christopher Nielsen, Sarah J. MacEachern, Nils D. Forkert:
Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. FAIMI/EPIMI@MICCAI 2024: 163-175 - 2023
- [j26]Alejandro Gutierrez, Anup Tuladhar, Matthias Wilms, Deepthi Rajashekar, Michael D. Hill, Andrew Demchuk, Mayank Goyal, Jens Fiehler, Nils D. Forkert:
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. Int. J. Comput. Assist. Radiol. Surg. 18(5): 827-836 (2023) - [j25]Banafshe Felfeliyan, Nils D. Forkert, Abhilash Rakkunedeth Hareendranathan, David Cornel, Yuyue Zhou, Gregor Kuntze, Jacob L. Jaremko, Janet Lenore Ronsky:
Self-supervised-RCNN for medical image segmentation with limited data annotation. Comput. Medical Imaging Graph. 109: 102297 (2023) - [j24]Jasmine A. Moore, Matthias Wilms, Alejandro Gutierrez, Zahinoor Ismail, Kayson Fakhar, Fatemeh Hadaeghi, Claus C. Hilgetag, Nils D. Forkert:
Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system. Frontiers Comput. Neurosci. 17 (2023) - [j23]Raissa Souza, Matthias Wilms, Milton Camacho, G. Bruce Pike, Richard Camicioli, Oury Monchi, Nils D. Forkert:
Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J. Am. Medical Informatics Assoc. 30(12): 1925-1933 (2023) - [j22]Jasmine A. Moore, Anup Tuladhar, Zahinoor Ismail, Pauline Mouches, Matthias Wilms, Nils D. Forkert:
Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. Neuroinformatics 21(1): 45-55 (2023) - [c58]Raissa Souza, Emma A. M. Stanley, Nils D. Forkert:
On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. CLIP/FAIMI/EPIMI@MICCAI 2023: 289-300 - [c57]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert:
An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. Computer-Aided Diagnosis 2023 - [c56]Emma A. M. Stanley, Matthias Wilms, Nils D. Forkert:
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis. MICCAI (2) 2023: 489-499 - [c55]Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho, Raissa Souza, Nils D. Forkert:
Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference. MIDL 2023: 1304-1317 - [i7]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert:
Towards objective and systematic evaluation of bias in medical imaging AI. CoRR abs/2311.02115 (2023) - 2022
- [j21]Lucas Lo Vercio, Rebecca M. Green, Samuel Robertson, Sienna Guo, Andreas Dauter, Marta Marchini, Marta Vidal-García, Xiang Zhao, Anandita Mahika, Ralph S. Marcucio, Benedikt Hallgrímsson, Nils D. Forkert:
Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images of Mouse Embryos Using Convolutional Neural Networks. IEEE Access 10: 105084-105100 (2022) - [j20]Jordan J. Bannister, Matthias Wilms, J. David Aponte, David C. Katz, Ophir D. Klein, Francois P. J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models. Artif. Intell. Medicine 134: 102425 (2022) - [j19]Hristina Uzunova, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
A systematic comparison of generative models for medical images. Int. J. Comput. Assist. Radiol. Surg. 17(7): 1213-1224 (2022) - [j18]Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert:
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. J. Am. Medical Informatics Assoc. 30(1): 112-119 (2022) - [j17]Kimberly Amador, Matthias Wilms, Anthony J. Winder, Jens Fiehler, Nils D. Forkert:
Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks. Medical Image Anal. 82: 102610 (2022) - [j16]Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
Localized Statistical Shape Models for Large-Scale Problems With Few Training Data. IEEE Trans. Biomed. Eng. 69(9): 2947-2957 (2022) - [j15]Jordan J. Bannister, Matthias Wilms, J. David Aponte, David C. Katz, Ophir D. Klein, Francois P. J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis. IEEE J. Biomed. Health Informatics 26(7): 3229-3239 (2022) - [j14]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE Trans. Medical Imaging 41(9): 2331-2347 (2022) - [c54]Gabrielle Dagasso, Matthias Wilms, Nils D. Forkert:
A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies. BIBM 2022: 3622-3628 - [c53]Anup Tuladhar, Jasmine A. Moore, Zahinoor Ismail, Nils D. Forkert:
Simulating progressive neurodegeneration in silico with deep artificial neural networks. CogSci 2022 - [c52]Banafshe Felfeliyan, Abhilash Rakkunedeth Hareendranathan, Gregor Kuntze, Stephanie Wichuk, Nils D. Forkert, Jacob L. Jaremko, Janet Lenore Ronsky:
Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss. ICPR Workshops (2) 2022: 603-617 - [c51]Alejandro Gutierrez, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. Computer-Aided Diagnosis 2022 - [c50]Samuel Robertson, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
Stroke lesion localization in 3D MRI datasets with deep reinforcement learning. Computer-Aided Diagnosis 2022 - [c49]Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert:
A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. Computer-Aided Diagnosis 2022 - [c48]Emma A. M. Stanley, Matthias Wilms, Nils D. Forkert:
Disproportionate Subgroup Impacts and Other Challenges of Fairness in Artificial Intelligence for Medical Image Analysis. EPIMI/ML-CDS@MICCAI 2022: 14-25 - [c47]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Sönke Langner, Nils D. Forkert:
Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model. MAD@MICCAI 2022: 95-107 - [c46]Christopher Nielsen, Anup Tuladhar, Nils D. Forkert:
Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks. OMIA@MICCAI 2022: 183-192 - [c45]Kimberly Amador, Anthony J. Winder, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging. MICCAI (3) 2022: 644-654 - [d1]Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert:
Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks. IEEE DataPort, 2022
- [i6]Banafshe Felfeliyan, Abhilash Rakkunedeth Hareendranathan, Gregor Kuntze, David Cornell, Nils D. Forkert, Jacob L. Jaremko, Janet Lenore Ronsky:
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation. CoRR abs/2207.11191 (2022) - [i5]Banafshe Felfeliyan, Abhilash Rakkunedeth Hareendranathan, Gregor Kuntze, Stephanie Wichuk, Nils D. Forkert, Jacob L. Jaremko, Janet Lenore Ronsky:
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss. CoRR abs/2209.08172 (2022) - 2021
- [j13]Anup Tuladhar, Jasmine A. Moore, Zahinoor Ismail, Nils D. Forkert:
Modeling Neurodegeneration in silico With Deep Learning. Frontiers Neuroinformatics 15: 748370 (2021) - [j12]Bryce A. Besler, Andrew S. Michalski, Michael T. Kuczynski, Aleena Abid, Nils D. Forkert, Steven K. Boyd:
Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets. Medical Image Anal. 67: 101887 (2021) - [j11]Samaneh Nobakht, Morgan Schaeffer, Nils D. Forkert, Sean M. Nestor, Sandra E. Black, Philip A. Barber, Alzheimer's Disease Neuroimaging Initiative:
Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol. Sensors 21(7): 2427 (2021) - [j10]Nagesh Subbanna, Matthias Wilms, Anup Tuladhar, Nils D. Forkert:
An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks. Sensors 21(11): 3874 (2021) - [c44]Hristina Uzunova, Jesse Kruse, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
Analysis of Generative Shape Modeling Approaches - Latent Space Properties and Interpretability. Bildverarbeitung für die Medizin 2021: 344-349 - [c43]Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert:
Federated Learning Using Variable Local Training for Brain Tumor Segmentation. BrainLes@MICCAI (2) 2021: 392-404 - [c42]Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. BrainLes@MICCAI (2) 2021: 420-432 - [c41]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows. MLCN@MICCAI 2021: 23-33 - [c40]Kimberly Amador, Matthias Wilms, Anthony J. Winder, Jens Fiehler, Nils D. Forkert:
Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks. MIDL 2021: 22-33 - [c39]Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils Daniel Forkert:
Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder. MIDL 2021: 497-506 - [i4]Bryce A. Besler, Tannis D. Kemp, Andrew S. Michalski, Nils D. Forkert, Steven K. Boyd:
Local Morphometry of Closed, Implicit Surfaces. CoRR abs/2108.04354 (2021) - [i3]Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert, Steven K. Boyd:
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry. CoRR abs/2111.01350 (2021) - 2020
- [j9]Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert:
Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks. IEEE Access 8: 94871-94879 (2020) - [j8]Anup Tuladhar, Sascha Gill, Zahinoor Ismail, Nils D. Forkert, Alzheimer's Disease Neuroimaging Initiative:
Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling. J. Biomed. Informatics 106: 103424 (2020) - [j7]Jordan J. Bannister, Sebastian Crites, J. David Aponte, David C. Katz, Matthias Wilms, Ophir D. Klein, Francois P. J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images. Sensors 20(11): 3171 (2020) - [j6]Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Clarissa L. Yasuda, Magdalena J. Sokolska, Hans Rolf Jäger, Nils Daniel Forkert:
Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA. IEEE Trans. Biomed. Eng. 67(7): 1936-1946 (2020) - [c38]Hristina Uzunova, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
Quantitative Comparison of Generative Shape Models for Medical Images. Bildverarbeitung für die Medizin 2020: 201-207 - [c37]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. MLCN/RNO-AI@MICCAI 2020: 23-33 - [c36]Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
A Kernelized Multi-level Localization Method for Flexible Shape Modeling with Few Training Data. MICCAI (4) 2020: 765-775 - [i2]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. CoRR abs/2011.13484 (2020)
2010 – 2019
- 2019
- [j5]Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Thomas W. Okell, Nils D. Forkert:
A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms. Medical Image Anal. 56: 184-192 (2019) - [j4]Jonathan Doucette, Luxi Wei, Enedino Hernández-Torres, Christian Kames, Nils D. Forkert, Rasmus Aamand, Torben Ellegaard Lund, Brian Hansen, Alexander Rauscher:
Rapid solution of the Bloch-Torrey equation in anisotropic tissue: Application to dynamic susceptibility contrast MRI of cerebral white matter. NeuroImage 185: 198-207 (2019) - [c35]Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils D. Forkert:
The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study. MLMECH/CVII-STENT@MICCAI 2019: 141-148 - 2018
- [j3]Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils Daniel Forkert:
Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images. IEEE Trans. Biomed. Eng. 65(7): 1486-1494 (2018) - [c34]Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils Daniel Forkert:
Robust cerebrovascular segmentation in 4D ASL MRA images. ISBI 2018: 1348-1351 - [c33]Bryce A. Besler, Leigh Gabel, Lauren A. Burt, Nils Daniel Forkert, Steven K. Boyd:
Bone Adaptation as Level Set Motion. MSKI@MICCAI 2018: 58-72 - [c32]Renzo Phellan, Thomas Lindner, Michael Helle, Thiago Vallin Spina, Alexandre X. Falcão, Nils Daniel Forkert:
Four-Dimensional ASL MR Angiography Phantoms with Noise Learned by Neural Styling. CVII-STENT/LABELS@MICCAI 2018: 131-139 - [i1]Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Bjoern H. Menze:
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes. CoRR abs/1803.09340 (2018) - 2017
- [c31]René Werner, Daniel Schetelig, Thilo Sothmann, Eike Mücke, Matthias Wilms, Bastian Cheng, Nils Daniel Forkert:
Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study. Bildverarbeitung für die Medizin 2017: 161-166 - [c30]Renzo Phellan, Thomas Lindner, Alexandre X. Falcão, Nils Daniel Forkert:
Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain. Computer-Aided Diagnosis 2017: 101341B - [c29]Sahand Talai, Kai Boelmans, Jan Sedlacik, Nils Daniel Forkert:
Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets. Computer-Aided Diagnosis 2017: 101342H - [c28]Javier Villafruela, Sebastian Crites, Bastian Cheng, Christian Knaack, Götz Thomalla, Bijoy K. Menon, Nils Daniel Forkert:
Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning. Computer-Aided Diagnosis 2017: 101342K - [c27]Anthony J. Winder, Susanne Siemonsen, Fabian Flottmann, Jens Fiehler, Nils Daniel Forkert:
Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention. Computer-Aided Diagnosis 2017: 101344B - [c26]Renzo Phellan, Alan Peixinho, Alexandre X. Falcão, Nils Daniel Forkert:
Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network. CVII-STENT/LABELS@MICCAI 2017: 39-46 - [c25]Bryce A. Besler, Andrew S. Michalski, Nils Daniel Forkert, Steven K. Boyd:
Automatic Full Femur Segmentation from Computed Tomography Datasets Using an Atlas-Based Approach. MSKI@MICCAI 2017: 120-132 - 2016
- [c24]René Werner, Matthias Wilms, Bastian Cheng, Nils Daniel Forkert:
Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM. PRNI 2016: 1-4 - 2015
- [j2]Nils Daniel Forkert:
Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets. it Inf. Technol. 57(3): 208-212 (2015) - [c23]Albrecht Kleinfeld, Oskar Maier, Nils Daniel Forkert, Heinz Handels:
Automatische Detektion von Okklusionen zerebraler Arterien in 3D-Magnetresonanzangiographiedaten. Bildverarbeitung für die Medizin 2015: 17-22 - [c22]Nils Daniel Forkert, Jens Fiehler:
Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier. Biomedical Applications in Molecular, Structural, and Functional Imaging 2015: 94172H - 2013
- [b1]Nils Daniel Forkert:
Model-Based Analysis of Cerebrovascular Diseases Combining 3D and 4D MRA Datasets. University of Hamburg, 2013 - [j1]Nils Daniel Forkert, Till Illies, Einar Goebell, Jens Fiehler, Dennis Säring, Heinz Handels:
Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations. Int. J. Comput. Assist. Radiol. Surg. 8(5): 775-786 (2013) - [p1]Nils Daniel Forkert:
Modellbasierte Analyse zerebrovaskulärer Erkrankungen durch Kombination von 3D und 4D MRA Datensätzen. Ausgezeichnete Informatikdissertationen 2013: 31-40 - 2012
- [c21]Nils Daniel Forkert, Alexander Schmidt-Richberg, Alexander Münchau, Jens Fiehler, Heinz Handels, Kai Boelmans:
Automatische atlasbasierte Differenzierung von klassischen und atypischen Parkinsonsyndromen. Bildverarbeitung für die Medizin 2012: 225-230 - [c20]Santiago Suniaga, René Werner, Andre Kemmling, Michael Groth, Jens Fiehler, Nils Daniel Forkert:
Automatische Detektion von Aneurysmen in 3D Time-of-Flight Magnetresonanzangiographie Datensätzen. GI-Jahrestagung 2012: 1738-1744 - [c19]Santiago Suniaga, René Werner, Andre Kemmling, Michael Groth, Jens Fiehler, Nils Daniel Forkert:
Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets. MLMI 2012: 63-69 - [c18]Nils Daniel Forkert, Santiago Suniaga, Jens Fiehler, Heike Wersching, Stefan Knecht, Andre Kemmling:
Generation of a Probabilistic Arterial Cerebrovascular Atlas Derived from 700 Time-of-Flight MRA Datasets. MIE 2012: 148-152 - 2011
- [c17]Nils Daniel Forkert, Alexander Schmidt-Richberg, Jan Ehrhardt, Jens Fiehler, Heinz Handels, Dennis Säring:
Vesselness-geführte Level-Set Segmentierung von zerebralen Gefäßen. Bildverarbeitung für die Medizin 2011: 8-12 - [c16]Tobias Verleger, Dennis Säring, Susanne Siemonsen, Jens Fiehler, Nils Daniel Forkert:
Segmentierung rekanalisierter Blutgefäße nach Lysetherapie unter Verwendung von Time-of-Flight MRA Datensätzen. GI-Jahrestagung 2011: 443 - [c15]Nils Daniel Forkert, Alexander Schmidt-Richberg, Brigitte Holst, Alexander Münchau, Heinz Handels, Kai Boelmans:
Image-based Classification of Parkinsonian Syndromes Using T2'-Atlases. MIE 2011: 465-469 - [c14]Nils Daniel Forkert, Dennis Säring, Till Illies, Jens Fiehler, Jan Ehrhardt, Heinz Handels, Alexander Schmidt-Richberg:
Direction-dependent level set segmentation of cerebrovascular structures. Image Processing 2011: 79623S - 2010
- [c13]Nils Daniel Forkert, Dennis Säring, Andrea Eisenbeis, Frank Leypoldt, Jens Fiehler, Heinz Handels:
Experimental Assessment of Infarct Lesion Growth in Mice using Time-Resolved T2* MR Image Sequences. Bildverarbeitung für die Medizin 2010: 330-334 - [c12]Nils Daniel Forkert, Alexander Schmidt-Richberg, Dennis Säring, Jens Fiehler, Till Illies, Dietmar P. F. Möller, Heinz Handels:
Graphen- und Level-Set-basierte Nachverarbeitung von 3D-Gefäßsegmentierungen. Bildverarbeitung für die Medizin 2010: 425-429 - [c11]Dennis Säring, Nils Daniel Forkert, Till Illies, Jens Fiehler, Heinz Handels:
Evaluation of Methods for Bolus Arrival Time Determination using a Four-dimensional MRA Flow Phantom. MedInfo 2010: 1263-1267 - [c10]Nils Daniel Forkert, Dennis Säring, Heinz Handels:
Automatic Analysis of the Anatomy of Arteriovenous Malformations using 3D and 4D MRA Image Sequences. MedInfo 2010: 1268-1272 - [c9]Nils Daniel Forkert, Alexander Schmidt-Richberg, Dennis Säring, Till Illies, Jens Fiehler, Heinz Handels:
Closing of interrupted vascular segmentations: an automatic approach based on shortest paths and level sets. Image Processing 2010: 76233G
2000 – 2009
- 2009
- [c8]Nils Daniel Forkert, Dennis Säring, Karolin Wenzel, Jens Fiehler, Till Illies, Dietmar P. F. Möller, Heinz Handels:
Automatische Segmentierung der zerebralen Gefäße aus 3D-TOF-MRA-Bildsequenzen mittels Fuzzy-Methoden. Bildverarbeitung für die Medizin 2009: 46-51 - [c7]Nils Daniel Forkert, Dennis Säring, Jens Fiehler, Till Illies, Heinz Handels:
AnToNIa: A Software Tool for the Hemodynamic Analysis of Cerebral Vascular Malformations Using 3D and 4D MRA Image Sequences. GI Jahrestagung 2009: 1249-1256 - [c6]Nils Daniel Forkert:
Analyse und dynamische 3D-Visualisierung des Blutflusses von zerebralen Gefäßstrukturen unter Verwendung von 3D- und 4D-Magnetresonanzangiographie-Bildfolgen. Informatiktage 2009: 223-226 - [c5]Nils Daniel Forkert, Dennis Säring, Karolin Wenzel, Till Illies, Jens Fiehler, Heinz Handels:
Fuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR Images. MIE 2009: 816-820 - [c4]Nils Daniel Forkert, Dennis Säring, Jens Fiehler, Till Illies, Dietmar P. F. Möller, Heinz Handels:
Analysis and dynamic 3D visualization of cerebral blood flow combining 3D and 4D MR image sequences. Image-Guided Procedures 2009: 726133 - 2008
- [c3]Nils Daniel Forkert, Dennis Säring, Jens Fiehler, Till Illies, Heinz Handels:
Automatische Lokalisation und hämodynamische Charakterisierung von Gefäßstrukturen bei arteriovenösen Malformationen. Bildverarbeitung für die Medizin 2008: 107-111 - [c2]Nils Daniel Forkert, Dennis Säring, Jens Fiehler, Till Illies, Matthias Färber, Dietmar P. F. Möller, Heinz Handels:
Fully Automatic Skull-Stripping in 3D Time-of-Flight MRA Image Sequences. VCBM 2008: 159-165 - 2007
- [c1]Dennis Säring, Jens Fiehler, Nils Daniel Forkert, Milena Piening, Heinz Handels:
Visual Computing zur Analyse von zerebralen arteriovenösen Malformationen in 3D- und 4D-MR Bilddaten. Bildverarbeitung für die Medizin 2007: 262-266
Coauthor Index
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