Publications

@article{pellegrini_machine_2018,

title = {Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review},

author = {Enrico Pellegrini and Lucía Ballerini and María del Carmen Valdés-Hernández and Francesca M Chappell and Victor González-Castro and Devasuda Anblagan and Samuel Danso and Susana Muñoz-Maniega and Dominic Job and Cyril Pernet},

url = {https://www.sciencedirect.com/science/article/pii/S2352872918300447},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring},

volume = {10},

pages = {519–535},

abstract = {Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.},

note = {Publisher: No longer published by Elsevier},

keywords = {Cerebrovascular Disease, Classification, Dementia, machine learning, MRI, Pathological Aging, segmentation, small vessel disease},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_application_2017,

title = {Application of texture analysis to study small vessel disease and blood–brain barrier integrity},

author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Francesca M Chappell and Eleni Sakka and Stephen Makin and Paul A Armitage and William H Nailon and Joanna M Wardlaw},

url = {https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2017.00327/full},

year  = {2017},

date = {2017-01-01},

journal = {Frontiers in neurology},

volume = {8},

pages = {327},

abstract = {We evaluate the alternative use of texture analysis for evaluating the role of blood–brain barrier (BBB) in small vessel disease (SVD).},

note = {Publisher: Frontiers Media SA},

keywords = {blood brain barrier, FLAIR Imaging, MRI, neuroimaging, small vessel disease},

pubstate = {published},

tppubtype = {article}

}

@article{valdes-hernandez_computational_2016,

title = {On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology},

author = {Maria del Carmen Valdés-Hernández and Victor González-Castro and Dina T Ghandour and Xin Wang and Fergus Doubal and Susana Muñoz-Maniega and Paul A Armitage and Joanna M Wardlaw},

url = {https://link.springer.com/article/10.1007/s00234-016-1648-3},

year  = {2016},

date = {2016-01-01},

journal = {Neuroradiology},

volume = {58},

pages = {475–485},

abstract = {Subtle inhomogeneities in the scanner’s magnetic fields (B0 and B1) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B1 inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {cerebrovascular disorders, leukoencephalopathies, MRI, neuroimaging, white matter hyperintensities},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_automatic_2016,

title = {Automatic rating of perivascular spaces in brain MRI using bag of visual words},

author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Paul A Armitage and Joanna M Wardlaw},

url = {https://link.springer.com/chapter/10.1007/978-3-319-41501-7_72},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings 13},

pages = {642–649},

abstract = {This paper presents a fully automatic method for assessing perivascular space (PVS) burden in the basal ganglia using structural MRI. A Support Vector Machine classifier, combined with a Bag of Visual Words (BoW) model, describes the region using two local descriptor approaches: SIFT and textons. The method achieves an accuracy of 82.34% with SIFT and 79.61% with textons, aiding in the study of neurological conditions linked to enlarged PVS.},

note = {Publisher: Springer International Publishing},

keywords = {machine learning, MRI, neurological disorders, perivascular spaces},

pubstate = {published},

tppubtype = {article}

}