@proceedings {990, title = {Assessing the variability of brain diffusion MRI preprocessing pipelines using a Region-of-Interest analysis}, volume = {5015}, year = {2023}, month = {2023}, abstract = {

The lack of a standardized preprocessing pipeline is a significant source of variability that might lower the reproducibility of studies, especially across sites and with incomplete description of the preprocessing workflows. We evaluate the downstream impact of variability in preprocessing workflow by quantifying the reproducibility and variability of region-of-interest (ROI) analyses. While many pipelines achieve excellent reproducibility in most ROI, we observed a large variability in performance of preprocessing workflows to the extent that some pipelines are detrimental to the data quality and reproducibility.

}, author = {Veraart, Jelle and Winzeck, Stephan and {\'A}lvaro Planchuelo-G{\'o}mez and Fricke, Bj{\"o}rn and Kornaropoulos, Evgenios N and Merisaari, Harri and Pieciak, Tomasz and Zou, Yukai and Descoteaux, Maxime} } @proceedings {985, title = {Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies}, volume = {2786}, year = {2023}, month = {2023}, abstract = {

This work gathers the results of the QuadD22 challenge, held in MICCAI 2022. We evaluate whether Deep Learning (DL) Techniques are able to improve the quality of diffusion MRI data in clinical studies. To that end, we focused on a real study on migraine, where the differences between groups are drastically reduced when using 21 gradient directions instead of 61. Thus, we asked the participants to augment dMRI data acquired with only 21 directions to 61 via DL. The results were evaluated using a real clinical study with TBSS in which we statistically compared episodic migraine to chronic migraine.

}, author = {Aja-Fernandez, Santiago and Martin-Martin, Carmen and Pieciak, Tomasz and {\'A}lvaro Planchuelo-G{\'o}mez and Faiyaz, Abrar and Uddin, Nasir and Tiwari, Abhishek and Shigwan, Saurabh J and Zheng, Tianshu and Cao, Zuozhen and Blumberg, Stefano B and Sen, Snigdha and Yigit Avci, Mehmet and Li, Zihan and Wang, Xinyi and Tang, Zihao and Rauland, Amelie and Merhof, Dorit and Manzano Maria, Renata and Campos, Vinicius P and HashemiazadehKolowri, SeyyedKazem and DiBella, Edward and Peng, Chenxu and Chen, Zan and Ullah, Irfan and Mani, Merry and Eckstrom, Samuel and Baete, Steven H and Scifitto, Scifitto and Singh, Rajeev Kumar and Wu, Dan and Goodwin-Allcock, Tobias and Slator, Paddy J and Bilgic, Berkin and Tian, Qiyuan and Cabezas, Mariano and Santini, Tales and Andrade da Costa Vieira, Marcelo and Shen, Zhimin and Abdolmotalleby, Hesam and Filipiak, Patryk and Tristan-Vega, Antonio and de Luis-Garcia, Rodrigo} } @article {995, title = {Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies}, journal = {NeuroImage: Clinical}, volume = {39}, year = {2023}, pages = {103483}, abstract = {

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

}, keywords = {Angular resolution, Artificial Intelligence, Deep learning, Diffusion tensor, diffusion MRI, machine learning}, issn = {2213-1582}, doi = {https://doi.org/10.1016/j.nicl.2023.103483}, url = {https://www.sciencedirect.com/science/article/pii/S2213158223001742}, author = {Santiago Aja-Fern{\'a}ndez and Carmen Mart{\'\i}n-Mart{\'\i}n and {\'A}lvaro Planchuelo-G{\'o}mez and Abrar Faiyaz and Md Nasir Uddin and Giovanni Schifitto and Abhishek Tiwari and Saurabh J. Shigwan and Rajeev Kumar Singh and Tianshu Zheng and Zuozhen Cao and Dan Wu and Stefano B. Blumberg and Snigdha Sen and Tobias Goodwin-Allcock and Paddy J. Slator and Mehmet Yigit Avci and Zihan Li and Berkin Bilgic and Qiyuan Tian and Xinyi Wang and Zihao Tang and Mariano Cabezas and Amelie Rauland and Dorit Merhof and Renata Manzano Maria and Vin{\'\i}cius Paran{\'\i}ba Campos and Tales Santini and Marcelo Andrade da Costa Vieira and SeyyedKazem HashemizadehKolowri and Edward DiBella and Chenxu Peng and Zhimin Shen and Zan Chen and Irfan Ullah and Merry Mani and Hesam Abdolmotalleby and Samuel Eckstrom and Steven H. Baete and Patryk Filipiak and Tanxin Dong and Qiuyun Fan and Rodrigo de Luis-Garc{\'\i}a and Antonio Trist{\'a}n-Vega and Tomasz Pieciak} } @article {912, title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge}, journal = {bioRxiv}, year = {2021}, month = {2021}, doi = {10.1101/2021.03.02.433228}, url = {https://www.biorxiv.org/content/early/2021/03/02/2021.03.02.433228}, author = {De Luca, Alberto and Ianus, Andrada and Leemans, Alexander and Palombo, Marco and Shemesh, Noam and Zhang, Hui and Alexander, Daniel C and Nilsson, Markus and Froeling, Martijn and Biessels, Geert-Jan and Zucchelli, Mauro and Frigo, Matteo and Albay, Enes and Sedlar, Sara and Alimi, Abib and Deslauriers-Gauthier, Samuel and Deriche, Rachid and Fick, Rutger and Maryam Afzali and Tomasz Pieciak and Bogusz, Fabian and Santiago Aja-Fern{\'a}ndez and Ozarslan, Evren and Derek K. Jones and Chen, Haoze and Jin, Mingwu and Zhang, Zhijie and Wang, Fengxiang and Nath, Vishwesh and Parvathaneni, Prasanna and Morez, Jan and Sijbers, Jan and Jeurissen, Ben and Fadnavis, Shreyas and Endres, Stefan and Rokem, Ariel and Garyfallidis, Eleftherios and Sanchez, Irina and Prchkovska, Vesna and Rodrigues, Paulo and Landman, Bennet A and Schilling, Kurt G} } @article {933, title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge}, journal = {NeuroImage}, year = {2021}, month = {2021}, pages = {118367}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2021.118367}, url = {https://www.sciencedirect.com/science/article/pii/S1053811921006431}, author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C. Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fern{\'a}ndez and Evren {\"O}zarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fengxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G. Schilling} } @article {martinez2020smartphone, title = {Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles}, journal = {Journal of Field Robotics}, volume = {37}, number = {3}, year = {2020}, pages = {404{\textendash}420}, author = {Martinez-Alpiste, Ignacio and Pablo Casaseca-de-la-Higuera and Jose-Maria Alcaraz-Calero and Grecos, Christos and Wang, Qi} } @conference {martinez2019benchmarking, title = {Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform}, booktitle = {2019 IEEE Wireless Communications and Networking Conference (WCNC)}, year = {2019}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Martinez-Alpiste, Ignacio and Pablo Casaseca-de-la-Higuera and Jose-Maria Alcaraz-Calero and Grecos, Christos and Wang, Qi} } @article {802, title = {Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming}, journal = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)}, volume = {15}, year = {2019}, pages = {17}, author = {Irondi, Iheanyi and Wang, Qi and Grecos, Christos and Calero, Jose M Alcaraz and Pablo Casaseca-de-la-Higuera} } @article {817, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, volume = {81}, year = {2019}, pages = {989{\textendash}1003}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @conference {800, title = {Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps}, booktitle = {Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018}, year = {2018}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Hoyos-Barcel{\'o}, Carlos and S. Merino-Caviedes and Wang, Qi and Luo, Chunbo and Wang, Xinheng and Wang, Zhi} } @article {793, title = {Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling}, journal = {Magnetic resonance in medicine}, year = {2018}, author = {{\'O}scar Pe{\~n}a-Nogales and Zhang, Yuxin and Wang, Xiaoke and Rodrigo de Luis-Garc{\'\i}a and Santiago Aja-Fern{\'a}ndez and Holmes, James H and Hernando, Diego} } @conference {mi2018towards, title = {Towards Optimal Power Splitting in Simultaneous Power and Information Transmission}, booktitle = {2018 IEEE Global Communications Conference (GLOBECOM)}, year = {2018}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Mi, Yang and Luo, Chunbo and Min, Geyong and Pablo Casaseca-de-la-Higuera and Wang, Zhi} } @article {768, title = {A novel infrared video surveillance system using deep learning based techniques}, journal = {Multimedia Tools and Applications}, volume = {77}, year = {2018}, chapter = {26657}, author = {Zhang, Huaizhong and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus and Pablo Casaseca-de-la-Higuera} } @conference {774, title = {Systematic infrared image quality improvement using deep learning based techniques}, booktitle = {Remote Sensing Technologies and Applications in Urban Environments}, year = {2016}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, author = {Zhang, Huaizhong and Pablo Casaseca-de-la-Higuera and Luo, Chunbo and Wang, Qi and Kitchin, Matthew and Parmley, Andrew and Monge-Alvarez, Jesus} } @article {627, title = {Adjugate Diffusion Tensors for Geodesic Tractography in White Matter}, journal = {Journal of Mathematical Imaging and Vision}, volume = {54}, year = {2015}, pages = {1{\textendash}14}, abstract = {

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise.

}, issn = {1573-7683}, doi = {10.1007/s10851-015-0586-8}, url = {http://dx.doi.org/10.1007/s10851-015-0586-8}, author = {Andrea Fuster and Tom Dela-Haije and Antonio Trist{\'a}n-Vega and Birgit Plantinga and Carl-Fredik Westin and Luc Florack} } @article {476, title = {Localized abnormalities in the cingulum bundle in patients with schizophrenia: A Diffusion Tensor tractography study}, journal = {NeuroImage: Clinical}, volume = {5}, year = {2014}, pages = {93{\textendash}99}, abstract = {

The cingulum bundle (CB) connects gray matter structures of the limbic system and as such has been implicated in the etiology of schizophrenia. There is growing evidence to suggest that the CB is actually comprised of a conglomeration of discrete sub-connections. The present study aimed to use Diffusion Tensor tractography to subdivide the CB into its constituent sub-connections, and to investigate the structural integrity of these sub-connections in patients with schizophrenia and matched healthy controls. Diffusion Tensor Imaging scans were acquired from 24 patients diagnosed with chronic schizophrenia and 26 matched healthy controls. Deterministic tractography was used in conjunction with FreeSurfer-based regions-of-interest to subdivide the CB into 5 sub-connections (I1 to I5). The patients with schizophrenia exhibited subnormal levels of FA in two cingulum sub-connections, specifically the fibers connecting the rostral and caudal anterior cingulate gyrus (I1) and the fibers connecting the isthmus of the cingulate with the parahippocampal cortex (I4). Furthermore, while FA in the I1 sub-connection was correlated with the severity of patients{\textquoteright} positive symptoms (specifically hallucinations and delusions), FA in the I4 sub-connection was correlated with the severity of patients{\textquoteright} negative symptoms (specifically affective flattening and anhedonia/asociality). These results support the notion that the CB is a conglomeration of structurally interconnected yet functionally distinct sub-connections, of which only a subset are abnormal in patients with schizophrenia. Furthermore, while acknowledging the fact that the present study only investigated the CB, these results suggest that the positive and negative symptoms of schizophrenia may have distinct neurobiological underpinnings.

}, author = {Whitford, Thomas J and Lee, Sun Woo and Oh, Jungsu S and Rodrigo de Luis-Garc{\'\i}a and Savadjiev, Peter and Alvarado, Jorge L and Carl-Fredik Westin and Niznikiewicz, Margaret and Nestor, Paul G and McCarley, Robert W} } @article {ruiz2013advanced, title = {Advanced signal processing methods for biomedical imaging}, journal = {International journal of biomedical imaging}, volume = {2013}, year = {2013}, publisher = {Hindawi Publishing Corporation}, author = {Juan Ruiz-Alzola and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {de2013geometrical, title = {Geometrical constraints for robust tractography selection}, journal = {NeuroImage}, volume = {81}, year = {2013}, pages = {26{\textendash}48}, publisher = {Academic Press}, author = {Rodrigo de Luis-Garc{\'\i}a and Carl-Fredik Westin and Carlos Alberola-Lopez} } @conference {tristan2012deblurring, title = {Deblurring of probabilistic ODFs in quantitative diffusion MRI}, booktitle = {Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on}, year = {2012}, pages = {932{\textendash}935}, publisher = {IEEE}, organization = {IEEE}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {tristan2012efficient, title = {Efficient and robust nonlocal means denoising of MR data based on salient features matching}, journal = {Computer methods and programs in biomedicine}, volume = {105}, number = {2}, year = {2012}, pages = {131{\textendash}144}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {tristan2012least, title = {Least squares for diffusion tensor estimation revisited: Propagation of uncertainty with Rician and non-Rician signals}, journal = {NeuroImage}, volume = {59}, number = {4}, year = {2012}, pages = {4032{\textendash}4043}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {casaseca2012optimal, title = {Optimal real-time estimation in diffusion tensor imaging}, journal = {Magnetic resonance imaging}, volume = {30}, number = {4}, year = {2012}, pages = {506{\textendash}517}, publisher = {Elsevier}, author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin and Raul San Jose-Estepar} } @inbook {de2012choice, title = {On the choice of a tensor distance for DTI white matter segmentation}, booktitle = {New Developments in the Visualization and Processing of Tensor Fields}, year = {2012}, pages = {283{\textendash}306}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {de2011gaussian, title = {Gaussian mixtures on tensor fields for segmentation: Applications to medical imaging}, journal = {Computerized Medical Imaging and Graphics}, volume = {35}, number = {1}, year = {2011}, pages = {16{\textendash}30}, publisher = {Elsevier}, author = {Rodrigo de Luis-Garc{\'\i}a and Carl-Fredik Westin and Carlos Alberola-Lopez} } @article {tristan2010new, title = {A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk{\textendash}Radon transform}, journal = {NeuroImage}, volume = {49}, number = {2}, year = {2010}, pages = {1301{\textendash}1315}, publisher = {Academic Press}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @article {martin2009addendum, title = {Addendum to {\textquotedblleft}Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data{\textquotedblright}[Medical Image Analysis 13 (2009) 19{\textendash}35]}, journal = {Medical image analysis}, volume = {13}, number = {6}, year = {2009}, pages = {910}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Emma Mu{\~n}oz-Moreno and Cammoun, Leila and J-P Thiran and Carl-Fredik Westin and Carlos Alberola-Lopez} } @conference {478, title = {Automatic segmentation of white matter structures from DTI using tensor invariants and tensor orientation}, booktitle = {Proc. Intl. Soc. Mag. Reson. Med}, year = {2009}, author = {Rodrigo de Luis-Garc{\'\i}a and Lopez, C Alberola and Kindlmann, G and Carl-Fredik Westin} } @inbook {tristan2009bias, title = {Bias of least squares approaches for diffusion tensor estimation from array coils in DT{\textendash}MRI}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {919{\textendash}926}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @inbook {tristan2009blurring, title = {On the Blurring of the Funk{\textendash}Radon Transform in Q{\textendash}Ball Imaging}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2009}, year = {2009}, pages = {415{\textendash}422}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Antonio Trist{\'a}n-Vega and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin} } @article {tristan2009estimation, title = {Estimation of fiber orientation probability density functions in high angular resolution diffusion imaging}, journal = {NeuroImage}, volume = {47}, number = {2}, year = {2009}, pages = {638{\textendash}650}, publisher = {Elsevier}, author = {Antonio Trist{\'a}n-Vega and Carl-Fredik Westin and Santiago Aja-Fern{\'a}ndez} } @inbook {de2009segmentation, title = {Segmentation of tensor fields: Recent advances and perspectives}, booktitle = {Tensors in Image Processing and Computer Vision}, year = {2009}, pages = {35{\textendash}58}, publisher = {Springer}, organization = {Springer}, author = {Rodrigo de Luis-Garc{\'\i}a and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {martin2009sequential, title = {Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data}, journal = {Medical image analysis}, volume = {13}, number = {1}, year = {2009}, pages = {19{\textendash}35}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Emma Mu{\~n}oz-Moreno and Cammoun, Leila and J-P Thiran and Carl-Fredik Westin and Carlos Alberola-Lopez} } @article {aja2008noise, title = {Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach}, journal = {Image Processing, IEEE Transactions on}, volume = {17}, number = {8}, year = {2008}, pages = {1383{\textendash}1398}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin} } @article {aja2008restoration, title = {Restoration of DWI data using a Rician LMMSE estimator}, journal = {Medical Imaging, IEEE Transactions on}, volume = {27}, number = {10}, year = {2008}, pages = {1389{\textendash}1403}, publisher = {IEEE}, author = {Santiago Aja-Fern{\'a}ndez and Niethammer, Marc and Kubicki, Marek and Martha E Shenton and Carl-Fredik Westin} } @inbook {niethammer2007outlier, title = {Outlier rejection for diffusion weighted imaging}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2007}, year = {2007}, pages = {161{\textendash}168}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Niethammer, Marc and Bouix, Sylvain and Santiago Aja-Fern{\'a}ndez and Carl-Fredik Westin and Martha E Shenton} } @article {martin2007sequential, title = {Sequential anisotropic Wiener filtering applied to 3D MRI data}, journal = {Magnetic resonance imaging}, volume = {25}, number = {2}, year = {2007}, pages = {278{\textendash}292}, publisher = {Elsevier}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Carl-Fredik Westin} } @inbook {aja2007signal, title = {Signal LMMSE estimation from multiple samples in MRI and DT-MRI}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2007}, year = {2007}, pages = {368{\textendash}375}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez and Carl-Fredik Westin} } @proceedings {aja2006image, title = {Image quality assessment based on local variance}, year = {2006}, pages = {4815{\textendash}4818}, author = {Santiago Aja-Fern{\'a}ndez and Raul San Jose-Estepar and Carlos Alberola-Lopez and Carl-Fredik Westin} } @inbook {westin2006tensor, title = {Tensor field regularization using normalized convolution and markov random fields in a bayesian framework}, booktitle = {Visualization and Processing of Tensor Fields}, year = {2006}, pages = {381{\textendash}398}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Carl-Fredik Westin and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Knutsson, Hans} } @article {palacios2005group, title = {Group-Slicer: A collaborative extension of 3D-Slicer}, journal = {Journal of Biomedical Informatics}, volume = {38}, year = {2005}, pages = {431{\textendash}442}, author = {Federico Simmross-Wattenberg and Palacios-Camarero, Cristina and Pablo Casaseca-de-la-Higuera and Miguel Angel Martin-Fernandez and Santiago Aja-Fern{\'a}ndez and Juan Ruiz-Alzola and Carl-Fredik Westin and Carlos Alberola-Lopez} } @article {ruiz2005kriging, title = {Kriging filters for multidimensional signal processing}, journal = {Signal Processing}, volume = {85}, number = {2}, year = {2005}, pages = {413{\textendash}439}, publisher = {Elsevier}, author = {Juan Ruiz-Alzola and Carlos Alberola-Lopez and Carl-Fredik Westin} } @inbook {ruiz2005landmark, title = {Landmark-Based Registration of Medical-Image Data}, booktitle = {Medical Image Analysis Methods, Edited by Lena Costaridou, CRC Press}, year = {2005}, author = {Juan Ruiz-Alzola and Suarez-Santana, E and Carlos Alberola-Lopez and Carl-Fredik Westin} } @inbook {martin20043d, title = {3D Bayesian regularization of diffusion tensor MRI using multivariate Gaussian Markov random fields}, booktitle = {Medical Image Computing and Computer-Assisted Intervention{\textendash}MICCAI 2004}, year = {2004}, pages = {351{\textendash}359}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Marcos Martin-Fernandez and Carl-Fredik Westin and Carlos Alberola-Lopez} } @conference {415, title = {Neural network fusion strategies for identifying breast masses}, booktitle = {IEEE International Conference on Neural Networks - Conference Proceedings}, year = {2004}, address = {Budapest}, abstract = {

In this work, we introduce the Perceptron Average neural network fusion strategy and implemented a number of other fusion strategies to identify breast masses in mammograms as malignant or benign with both balanced and imbalanced input features. We numerically compare various fixed and trained fusion rules, i.e., the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, when applying them to a binary statistical pattern recognition problem. To judge from the experimental results, the Weighted Average approach outperforms the other fusion strategies with balanced input features, while the Perceptron Average is superior and achieves the goals with lowest standard deviation with imbalanced ensembles. We concretely analyze the results of above fusion strategies, state the advantages of fusing the component networks, and provide our particular broad sense perspective about information fusion in neural networks.

}, keywords = {Biological organs, Breast cancers, Component neural networks (CNN), Image segmentation, Information fusions, Learning algorithms, Linear systems, Mammography, Mathematical models, Multilayer neural networks, Pattern recognition, Posterior probabilities, Tumors}, isbn = {0780383591}, doi = {10.1109/IJCNN.2004.1381010}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-10844231826\&partnerID=40\&md5=2be794a5832413fed34152d61dd49388}, author = {Y Wu and J He and Y Man and J I Arribas} } @inbook {jose2003lncs, title = {Freehand Ultrasound Reconstruction Based on ROI Prior Modeling and Normalized Convolution}, booktitle = {Lecture Notes in Computer Science}, volume = {2879}, year = {2003}, pages = {382{\textendash}390}, publisher = {Berlin: Springer-Verlag, 1973-}, organization = {Berlin: Springer-Verlag, 1973-}, author = {Raul San Jose-Estepar and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Ellsmere, James and Kikinis, Ron and Carl-Fredik Westin} } @inbook {jose2003freehand, title = {Freehand Ultrasound Reconstruction Based on ROI Prior Modeling and Normalized Convolution}, booktitle = {Lecture Notes in Computer Science}, volume = {2879}, year = {2003}, pages = {382{\textendash}390}, publisher = {Berlin: Springer-Verlag, 1973-}, organization = {Berlin: Springer-Verlag, 1973-}, author = {Raul San Jose-Estepar and Marcos Martin-Fernandez and Carlos Alberola-Lopez and Ellsmere, James and Kikinis, Ron and Carl-Fredik Westin} } @conference {413, title = {Fusing Output Information in Neural Networks: Ensemble Performs Better}, booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings}, year = {2003}, address = {Cancun}, abstract = {

A neural network ensemble is a learning paradigm where a finite number of component neural networks are trained for the same task. Previous research suggests that an ensemble as a whole is often more accurate than any of the single component networks. This paper focuses on the advantages of fusing different nature network architectures, and to determine the appropriate information fusion algorithm in component neural networks by several approaches within hard decision classifiers, when solving a binary pattern recognition problem. We numerically simulated and compared the different fusion approaches in terms of the mean-square error rate in testing data set, over synthetically generated binary Gaussian noisy data, and stated the advantages of fusing the hard outputs of different component networks to make a final hard decision classification. The results of the experiments indicate that neural network ensembles can indeed improve the overall accuracy for classification problems; in all fusion architectures tested, the ensemble correct classification rates are better than those achieved by the individual component networks. Finally we are nowadays comparing the above mentioned hard decision classifiers with new soft decision classifier architectures that make use of the additional continuous type intermediate network soft outputs, fulfilling probability fundamental laws (positive, and add to unity), which can be understood as the a posteriori probabilities of a given pattern to belong to a certain class.

}, keywords = {Algorithms, Backpropagation, Classification (of information), Computer simulation, Decision making, Estimation, Gaussian noise (electronic), Information fusions, Mathematical models, Medical imaging, Model selection, Multilayer neural networks, Neural network ensembles, Pattern recognition, Probability, Probability estimation, Problem solving, Regularization, Statistical methods, Statistical pattern recognition, Vectors}, doi = {https://doi.org/10.1109/IEMBS.2003.1280254}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-1542301061\&partnerID=40\&md5=32dbadb3b6ac3c6ae1ea33d89b52c75f}, author = {Y Wu and J I Arribas} } @inbook {ruiz2003geostatistical, title = {Geostatistical medical image registration}, booktitle = {Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003}, year = {2003}, pages = {894{\textendash}901}, publisher = {Springer}, organization = {Springer}, author = {Juan Ruiz-Alzola and Suarez, Eduardo and Carlos Alberola-Lopez and Warfield, Simon K and Carl-Fredik Westin} } @inbook {martin2003regularization, title = {Regularization of Diffusion Tensor Maps Using a Non-Gaussian Markov Random Field Approach}, booktitle = {Lecture Notes in Computer Science}, volume = {2879}, year = {2003}, pages = {92{\textendash}100}, publisher = {Berlin: Springer-Verlag}, organization = {Berlin: Springer-Verlag}, author = {Marcos Martin-Fernandez and Carlos Alberola-Lopez and Juan Ruiz-Alzola and Carl-Fredik Westin} } @inbook {martin2003novel, title = {A novel Gauss-Markov random field approach for regularization of diffusion tensor maps}, booktitle = {Computer Aided Systems Theory-EUROCAST 2003}, year = {2003}, pages = {506{\textendash}517}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Marcos Martin-Fernandez and Raul San Jose-Estepar and Carl-Fredik Westin and Carlos Alberola-Lopez} } @conference {412, title = {Estimates of constrained multi-class a posteriori probabilities in time series problems with neural networks}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1999}, publisher = {IEEE, United States}, organization = {IEEE, United States}, address = {Washington, DC, USA}, abstract = {

In time series problems, where time ordering is a crucial issue, the use of Partial Likelihood Estimation (PLE) represents a specially suitable method for the estimation of parameters in the model. We propose a new general supervised neural network algorithm, Joint Network and Data Density Estimation (JNDDE), that employs PLE to approximate conditional probability density functions for multi-class classification problems. The logistic regression analysis is generalized to multiple class problems with softmax regression neural network used to model the a-posteriori probabilities such that they are approximated by the network outputs. Constraints to the network architecture, as well as to the model of data, are imposed, resulting in both a flexible network architecture and distribution modeling. We consider application of JNDDE to channel equalization and present simulation results.

}, keywords = {Approximation theory, Computer simulation, Constraint theory, Data structures, Joint network-data density estimation (JNDDE), Mathematical models, Multi-class a posteriori probabilities, Neural networks, Partial likelihood estimation (PLE), Probability density function, Regression analysis}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0033325263\&partnerID=40\&md5=8c6134020b0b2a9c5ab05b131c070b88}, author = {J I Arribas and Jes{\'u}s Cid-Sueiro and T Adali and H Ni and B Wang and A R Figueiras-Vidal} }