Publications

@article{humphreys_protocol_2019,

title = {A protocol for precise comparisons of small vessel disease lesions between ex vivo magnetic resonance imaging and histopathology},

author = {Catherine A Humphreys and Maurits A Jansen and Susana Muñoz-Maniega and Víctor González-Castro and Cyril Pernet and Ian J Deary and Rustam Al-Shahi Salman and Joanna M Wardlaw and Colin Smith},

url = {https://journals.sagepub.com/doi/full/10.1177/1747493018799962},

year  = {2019},

date = {2019-01-01},

journal = {International Journal of Stroke},

volume = {14},

number = {3},

pages = {310–320},

abstract = {This study aims to improve understanding of the pathophysiology of human sporadic cerebral small vessel disease by correlating neuroimaging findings with neuropathological features. The research involves collecting histological samples from brain regions commonly affected by the disease, scanning them using 7-tesla MRI, and processing them for histology. The primary goal is to define the cellular characteristics of small vessel disease lesions and compare them to neuroimaging data. Secondary outcomes focus on abnormalities in protein expression and creating a reproducible protocol for correlating radiological and histological findings, which could apply to other neurological conditions in the future.},

note = {Publisher: SAGE Publications Sage UK: London, England},

keywords = {histology, neuroimaging, pathophysiology, small vessel disease},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_automatic_2016,

title = {Automatic recognition of fundamental tissues on histology images of the human cardiovascular system},

author = {Claudia Mazo and María Trujillo and Enrique Alegre and Liliana Salazar},

url = {https://www.sciencedirect.com/science/article/pii/S0968432816301573},

year  = {2016},

date = {2016-01-01},

journal = {Micron},

volume = {89},

pages = {1–8},

abstract = {This paper presents an automatic method for classifying fundamental tissues in histological images using k-means clustering. It identifies epithelial, connective, and muscle tissues with high sensitivity (0.79–0.91) and expert validation (4.82–4.85/5), supporting medical diagnosis and education.},

note = {Publisher: Pergamon},

keywords = {histology, machine learning, medical imaging, tissue classification},

pubstate = {published},

tppubtype = {article}

}