Nuestros investigadores

Mikel Ariz Galilea

Líneas de investigación
Biomedical Image Analysis; 3D Models; Pattern Recognition; Microscopy
Índice H
6, (Google Scholar, 04/02/2021)

Publicaciones científicas más recientes (desde 2010)

Autores: Anguiano, M.; Morales, X. ; Castilla Ruíz, Carlos; et al.
Revista: PLOS ONE
ISSN 1932-6203  Vol. 15  Nº 1  2020 
The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing. Here, we present a comprehensive, quantitative analysis of the role of cell-ECM interactions in cancer cell migration within a highly physiological environment consisting of mixed Matrigel-collagen hydrogel scaffolds of increasing complexity that mimic the tumor microenvironment at the leading edge of cancer invasion. We quantitatively show that the presence of Matrigel increases hydrogel stiffness, which promotes beta 1 integrin expression and metalloproteinase activity in H1299 lung cancer cells. Then, we show that ECM remodeling activity causes matrix alignment and compaction that favors higher tractions exerted by the cells. However, these traction forces do not linearly translate into increased motility due to a biphasic role of cell adhesions in cell migration: at low concentration Matrigel promotes migration-effective tractions exerted through a high number of small sized focal adhesions. However, at high Matrigel concentration, traction forces are exerted through fewer, but larger focal adhesions that favor attachment yielding lower cell motility.
Autores: Jiménez Sánchez, Daniel; Ariz Galilea, Mikel; Morgado, J. M. ; et al.
ISSN 1367-4803  Vol. 36  Nº 5  2020  págs. 1590 - 1598
Motivation: Recent advances in multiplex immunostaining and multispectral cytometry have opened the door to simultaneously visualizing an unprecedented number of biomarkers both in liquid and solid samples. Properly unmixing fluorescent emissions is a challenging task, which normally requires the characterization of the individual fluorochromes from control samples. As the number of fluorochromes increases, the cost in time and use of reagents becomes prohibitively high. Here, we present a fully unsupervised blind spectral unmixing method for the separation of fluorescent emissions in highly mixed spectral data, without the need for control samples. To this end, we extend an existing method based on non-negative Matrix Factorization, and introduce several critical improvements: initialization based on the theoretical spectra, automated selection of 'sparse' data and use of a re-initialized multilayer optimizer. Results: Our algorithm is exhaustively tested using synthetic data to study its robustness against different levels of colocalization, signal to noise ratio, spectral resolution and the effect of errors in the initialization of the algorithm. Then, we compare the performance of our method to that of traditional spectral unmixing algorithms using novel multispectral flow and image cytometry systems. In all cases, we show that our blind unmixing algorithm performs robust unmixing of highly spatially and spectrally mixed data with an unprecedently low computational cost. In summary, we present the first use of a blind unmixing method in multispectral flow and image cytometry, opening the door to the widespread use of our method to efficiently pre-process multiplex immunostaining samples without the need of experimental controls.
Autores: Ariz Galilea, Mikel; Abad, R. C. ; Castellanos, G. ; et al.
ISSN 0278-0062  Vol. 38  Nº 3  2019  págs. 813 - 823
We present a dynamic atlas composed of neuromelanin-enhancedmagnetic resonance brain images of 40 healthy subjects. The performance of this atlas is evaluated on the fully automated segmentation of two paired neuromelanin-rich brainstem healthy structures: the substantia nigra pars compacta and the locus coeruleus. We show that our dynamic atlas requires in average 60% less images and, therefore, 60% less computation time than a static multi-image atlas while achieving a similar segmentation performance. Then, we show that by applying our dynamic atlas, composed of healthy subjects, to the segmentation and neuromelanin quantification of a set of brain images of 39 Parkinson disease patients, we are able to find significant quantitative differences in the level of neuromelanin between healthy subjects and Parkinson disease patients, thus opening the door to the use of these structures as image biomarkers in future computer aided diagnosis systems for the diagnosis of Parkinson disease.
Autores: Neri, L. ; Dominguez, V.; Elurbide Tardio, Jasmin; et al.
ISSN 0168-8278  Vol. 68  Nº Supl. 1  2018  págs. S54 - S54
Autores: Anguiano Salcedo, María; Morales Urteaga, Xabier; Ariz Galilea, Mikel; et al.
ISSN 0008-5472  Vol. 78  Nº 13 Supl.  2018  págs. 178 - 178




Mikel Ariz was graduated with honors in Telecommunication Engineering by the Public University of Navarra (UPNA) in 2008. He spent 10 months during 2005/06 academic year at the Institut National Polytechnique de Grenoble, in France. In 2009, he was awarded one of the 20 'Becas Navarra 2009', valued in 40.000 euros and part of the 'Plan Internacional de Navarra (PIN) 2008-2011', whose goal was that the best academic records of universities in Navarra were able to do postgraduate studies in foreign universities of excellence. He graduated in the Master of Biomedical Engineering of the University of Melbourne, Australia, in 2010, standing out the realization of a final research project in diffusion magnetic resonance imaging at the renowned Florey Institute of Neuroscience. In 2011 he was awarded a grant 'Formación de Profesorado Universitario (FPU)' from the Ministry of Economy and Competitivity of the Government of Spain, for carrying out Ph.D. studies at the Public University of Navarra. He got his Ph.D. in 2016 with a thesis entitled 'Contributions to Head Pose Estimation Methods', in the field of image processing and computer vision. He did a two-month research stay in 2012 at the Imperial College London, under the supervision of Dr. Maja Pantic from the i-BUG group. He is, since 2015, a research technician at the Imaging Platform of the Center for Applied Medical Research (CIMA), mainly developing algorithms for the analysis and quantification of medical images. He is involved in several research projects, and he is an associate professor at the Department of Histology and Pathological Anatomy and at the Engineering School (TECNUN) of the University of Navarra.