Publikationer av Rodrigo Moreno
Refereegranskade
Artiklar
[1]
M. Siegbahn et al., "Asymmetry in Cortical Thickness of the Heschl's Gyrus in Unilateral Ear Canal Atresia," Otology and Neurotology, vol. 45, no. 4, s. 342-350, 2024.
[2]
C. Dartora et al., "A deep learning model for brain age prediction using minimally preprocessed T1w images as input," Frontiers in Aging Neuroscience, vol. 15, 2023.
[3]
J. Fu et al., "Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration," Human Brain Mapping, vol. 44, no. 4, s. 1289-1308, 2023.
[4]
A. Hain, D. Jörgens och R. Moreno, "Randomized iterative spherical‐deconvolution informed tractogram filtering," NeuroImage, vol. 278, 2023.
[5]
M. Siegbahn, C. Engmer Berglin och R. Moreno, "Automatic segmentation of the core of the acoustic radiation in humans," Frontiers in Neurology, vol. 13, 2022.
[6]
B. Klintström et al., "Photon-counting detector CT and energy-integrating detector CT for trabecular bone microstructure analysis of cubic specimens from human radius," European radiology experimental, vol. 6, no. 1, 2022.
[7]
F. Sinzinger et al., "Predicting the trabecular bone apparent stiffness tensor with spherical convolutional neural networks," Bone Reports, vol. 16, s. 101179, 2022.
[8]
F. Sinzinger et al., "Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients," Frontiers in Oncology, vol. 12, 2022.
[9]
I. Guha et al., "A comparative study of trabecular bone micro-structural measurements using different CT modalities," Physics in Medicine and Biology, vol. 65, no. 23, s. 235029, 2020.
[10]
L. Tanzi et al., "Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach," European Journal of Radiology, vol. 133, 2020.
[11]
L. Tanzi et al., "X-Ray Bone Fracture Classification Using Deep Learning : A Baseline for Designing a Reliable Approach," Applied Sciences, vol. 10, no. 4, 2020.
[12]
E. Klintström et al., "Direct estimation of human trabecular bone stiffness using cone beam computed tomography," Oral surgery, oral medicine, oral pathology and oral radiology, vol. 126, no. 1, s. 72-82, 2018.
[13]
N. Batool et al., "Estimation of trabecular bone thickness in gray scale : a validation study," International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. Supplement 1, 2017.
[14]
S. Koppal et al., "Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis," Magnetic Resonance in Medicine, vol. 78, no. 1, s. 285-296, 2017.
[15]
E. Klintström et al., "Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an In Vitro Study Using Finite Element Analysis," PLOS ONE, vol. 11, no. 8, 2016.
[16]
R. Moreno och Ö. Smedby, "Gradient-Based Enhancement of Tubular Structures in Medical Images," Medical Image Analysis, vol. 26, no. 1, s. 19-29, 2015.
[17]
R. Moreno, Ö. Smedby och D. Pahr, "Prediction of Apparent Trabecular Bone Stiffness through Fourth-Order Fabric Tensors," Biomechanics and Modeling in Mechanobiology, 2015.
Konferensbidrag
[18]
Y. Zhou et al., "Synthesis of Pediatric Brain Tumor Images With Mass Effect," i Medical Imaging 2023 : Image Processing, 2023.
[19]
F. Sinzinger, D. Pahr och R. Moreno, "Predicting The Trabecular Bone Stiffness Tensor with Spherical Convolutional Neural Networks," i Book of Abstracts of the 25th Congress of the European Society of Biomechanics, 2019.
[20]
D. Jörgens et al., "Towards a deep learning model for diffusion-aware tractogram filtering," i ISMRM 27th Annual Meeting & Exhibition, 11-16 May 2019, Montréal, QC, Canada, 2019.
[21]
I. Brusini et al., "Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry," i International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018; Granada; Spain; 20 September 2018 through 20 September 2018, 2019, s. 345-357.
[22]
I. Brusini et al., "Dependency of neural tracts' curvature estimations on tractography methods," i Human Brain Project Student Conference, 2017.
[23]
M. Platten et al., "Estimation of trabecular thickness in grayscale : an in vivo study," i ESSR 2017 / P-0196, 2017.
[24]
B. Klintström et al., "Feature space clustering for trabecular bone segmentation," i 20th Scandinavian Conference on Image Analysis, SCIA 2017, 2017, s. 65-75.
[25]
M. Chowdhury et al., "Granulometry-based trabecular bone segmentation," i 20th Scandinavian Conference on Image Analysis, SCIA 2017, 2017, s. 100-108.
[26]
I. Brusini et al., "Influence of Tractography Algorithms and Settings on Local Curvature Estimations," i Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2017.
[27]
M. Chowdhury et al., "Segmentation of Cortical Bone using Fast Level Sets," i MEDICAL IMAGING 2017 : IMAGE PROCESSING, 2017.
[28]
M. Chowdhury et al., "An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network," i 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, s. 3134-3139.
[29]
D. Jörgens, Ö. Smedby och R. Moreno, "Steering second-order tensor voting by vote clustering," i IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, s. 1245-1248.
[30]
R. Moreno och Ö. Smedby, "Vesselness Estimation through Higher-Order Orientation Tensors," i International Symposium on Biomedical Imaging (ISBI), 2016, s. 1139-1142.
[31]
R. Moreno, C. Wang och Ö. Smedby, "Vessel wall segmentation using implicit models and total curvature penalizers," i Scandinavian Conference on Image Analysis, 2013, s. 299-308.
Kapitel i böcker
[32]
D. Jörgens, D. Maxime och R. Moreno, "Challenges for tractogram filtering," i Anisotropy AcrossFields and Scales, Evren Özarslan · Thomas Schultz · Eugene Zhang · Andrea Fuster red., Switzerland : Springer, 2021, s. 149-168.
[33]
R. Moreno, P. Segers och C. Debbaut, "Estimation of the permeability tensor of the microvasculature of the liver through fabric tensors," i Computational Biomechanics for Medicine : From Algorithms to Models and Applications, : Springer, 2017, s. 71-79.
[34]
D. Jörgens och R. Moreno, "Towards grey scale-based tensor voting for blood vessel analysis," i Modeling, Analysis, and Visualization of Anisotropy, : Springer Berlin/Heidelberg, 2017, s. 145-173.
[35]
R. Moreno et al., "Anisotropy Estimation of Trabecular Bone in Gray-Scale : Comparison Between Cone Beam and Micro Computed Tomography Data," i Developments in Medical Image Processing and Computational Vision, Tavares, Joao Manuel R. S.; Natal Jorge, Renato red., : Springer, 2015, s. 207-220.
[36]
D. Jörgens och R. Moreno, "Tensor Voting : Current State, Challenges and New Trends in the Context of Medical Image Analysis," i Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, Ingrid Hotz and Thomas Schultz red., : Springer Science+Business Media B.V., 2015, s. 163-187.
Icke refereegranskade
Konferensbidrag
[37]
D. Jörgens, Ö. Smedby och R. Moreno, "Clustering of tensor votes for inference of fibre orientations in DTI data," i Proc. Swedish Society of Image Analysis (SSBA), 2016.
Kapitel i böcker
[38]
D. Jörgens, Ö. Smedby och R. Moreno, "Learning a single step of streamline tractography based on neural networks," i Computational Diffusion MRI : MICCAI Workshop on Computational Diffusion MRI, CDMRI 2017, Quebec, 10 September 2017, : Springer Nature, 2018, s. 103-116.
Övriga
[39]
[40]
D. Jörgens et al., "Merging label sources and multiple modalities in a deep neural network for tractogram filtering," (Manuskript).
[41]
M. Siegbahn et al., "Unilateral Ear Canal Atresia:A Study ofCortical Morphologyand Functional Connectivity," (Manuskript).
Senaste synkning med DiVA:
2024-04-23 00:30:21