Daniel Jörgens
Title
Cited by
Cited by
Year
Tractography and machine learning: Current state and open challenges
P Poulin, D Jörgens, PM Jodoin, M Descoteaux
Magnetic resonance imaging 64, 37-48, 2019
262019
Learning a single step of streamline tractography based on neural networks
D Jörgens, Ö Smedby, R Moreno
Computational Diffusion MRI, 103-116, 2018
82018
Challenges for tractogram filtering
D Jörgens, M Descoteaux, R Moreno
Anisotropy Across Fields and Scales, 149-168, 2021
32021
Segmentation of cortical bone using fast level sets
M Chowdhury, D Jörgens, C Wang, Ö Smedby, R Moreno
Medical Imaging 2017: Image Processing 10133, 1013327, 2017
32017
Influence of tractography algorithms and settings on local curvature estimations
I Brusini, D Jörgens, Ö Smedby, R Moreno
Proceedings of OHBM Annual Meeting, 2017
22017
Tensor voting: Current state, challenges and new trends in the context of medical image analysis
D Jörgens, R Moreno
Visualization and Processing of Higher Order Descriptors for Multi-Valued …, 2015
22015
Voxel-wise clustering of tractography data for building atlases of local fiber geometry
I Brusini, D Jörgens, Ö Smedby, R Moreno
International Conference on Medical Image Computing and Computer-Assisted …, 2019
12019
Towards a deep learning model for diffusion-aware tractogram filtering
D Jörgens, P Poulin, R Moreno, PM Jodoin, M Descoteaux
ISMRM 27th Annual Meeting & Exhibition, 11-16 May 2019, Montréal, QC, Canada, 2019
12019
Steering second-order tensor voting by vote clustering
D Jörgens, Ö Smedby, R Moreno
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1245-1248, 2016
12016
Merging label sources and multiple modalities in a deep neural network for tractogram filtering
D Jörgens, PM Jodoin, M Descoteaux, R Moreno
2020
Unilateral Ear Canal Atresia: A Study ofCortical Morphologyand Functional Connectivity
M Siegbahn, D Jörgens, K Zantop, C Engmér Berglin, M Hultcrantz, ...
2020
Development and application of rule- and learning-based approaches within the scope of neuroimaging: Tensor voting, tractography and machine learning
D Jörgens
KTH Royal Institute of Technology, 2020
2020
Bias in Machine Learning und Konsequenzen für die Anwendung in der Marktforschung
D Jörgens, Y Rieder, F Sinzinger
Marktforschung für die Smart Data World, 137-156, 2020
2020
Dependency of Neural Tracts’ Curvature Estimations on Tractography Methods
I Brusini, D Jörgens, Ö Smedby, R Moreno
1st Human Brain Project Student Conference, 33, 2017
2017
Towards Grey Scale-Based Tensor Voting for Blood Vessel Analysis
D Jörgens, R Moreno
Modeling, Analysis, and Visualization of Anisotropy, 145-173, 2017
2017
Clustering of tensor votes for inference of fibre orientations in DTI data
D Jörgens, Ö Smedby, R Moreno
Proc. Swedish Society of Image Analysis (SSBA), 2016
2016
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