Open Access
Contributions of artificial intelligence to low resolution renal multiparametric magnetic resonance analysis
(2021) Oyarzun Domeño, Anne; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Arterial spin labeling (ASL), is a Multiparametric Magnetic Resonance Imaging (MRI) technique used to quantify and evaluate Renal Blood Flow (RBF) and detect perfusion failure by labelling blood water as it flows throughout the kidney. This study aims at providing an automatic quantifying and evaluation tool for Chronic Kidney Disease (CKD) patients’s follow-up.
Open Access
Beyond basic tuning: exploring discrepancies in user and setup calibration for gaze estimation
(ACM, 2024-06-04) Garde Lecumberri, Gonzalo; Armendáriz Armenteros, José María; Beruete Cerezo, Rubén; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
Calibrating gaze estimation models is crucial to maximize the effectiveness of these systems, although its implementation also poses challenges related to usability. Therefore, the simplification of this process is key. In this work, we dissect the impact of calibration due to both the environment and the user in gaze estimation models that employ general-purpose devices. We aim to replicate a workflow close to the final application by starting with pre-trained models and subsequently calibrating them using different strategies, testing under various camera arrangements and user-specific variability. The results indicate differentiation between the impact due to the user and the setup, being the components due to the users a slightly more pronounced impact than those related to the setup, opening the door to understanding calibration as a composite process. In any case, the development of calibration-free remote gaze estimation solutions remains a great challenge, given the crucial role of calibration.
Open Access
An image-based framework for the analysis of the murine microvasculature: from tissue clarification to computational hemodynamics
(MDPI, 2022) Mañosas Sánchez, Santiago; Sanz Muñoz, Aritz; Ederra, Cristina; Urbiola, Ainhoa; Rojas De Miguel, Elvira; Ostiz, Ainhoa; Cortés Domínguez, Iván; Ramírez, Natalia; Ortiz de Solórzano, Carlos; Villanueva Larre, Arantxa; Malvè, Mauro; Ingeniería; Ingeniaritza
The blood–brain barrier is a unique physiological structure acting as a filter for every molecule reaching the brain through the blood. For this reason, an effective pharmacologic treatment supplied to a patient by systemic circulation should first be capable of crossing the barrier. Standard cell cultures (or those based on microfluidic devices) and animal models have been used to study the human blood–brain barrier. Unfortunately, these tools have not yet reached a state of maturity because of the complexity of this physiological process aggravated by a high heterogeneity that is not easily recapitulated experimentally. In fact, the extensive research that has been performed and the preclinical trials carried out provided sometimes contradictory results, and the functionality of the barrier function is still not fully understood. In this study, we have combined tissue clarification, advanced microscopy and image analysis to develop a one-dimensional computational model of the microvasculature hemodynamics inside the mouse brain. This model can provide information about the flow regime, the pressure field and the wall shear stress among other fluid dynamics variables inside the barrier. Although it is a simplified model of the cerebral microvasculature, it allows a first insight on into the blood–brain barrier hemodynamics and offers several additional possibilities to systematically study the barrier microcirculatory processes.
Open Access
Fast and robust ellipse detection algorithm for head-mounted eye tracking systems
(Springer, 2018) Martinikorena Aranburu, Ion; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Urtasun, Iñaki; Larumbe Bergera, Andoni; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.
Open Access
Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems
(ACM, 2018) Larumbe Bergera, Andoni; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.
Open Access
Multiparametric renal magnetic resonance imaging: a reproducibility study in renal allografts with stable function
(Wiley, 2023) Echeverría Chasco, Rebeca; Martín Moreno, Paloma L.; Vidorreta, Marta; Aramendía Vidaurreta, Verónica; Cano, David; Villanueva Larre, Arantxa; Bastarrika, Gorka; Fernández Seara, María A.; García Fernández, Nuria; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction (f), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f, and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = 0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.
Open Access
Attention to product images in an online retailing store: an eye-tracking study considering consumer goals and type of product
(California State University Press, 2022) Chocarro Eguaras, Raquel; Cortiñas Ugalde, Mónica; Villanueva Larre, Arantxa; Institute for Advanced Research in Business and Economics - INARBE; Institute of Smart Cities - ISC
The visual content of the product area is crucial in an e-commerce site. This paper studies the differences in attention to product images in the product area in e-commerce sites considering the effects of purchase stage and product category. Attention to product images on websites is measured using eye-tracking in two experiments with 58 students and 66 subjects, with four product categories and four purchase tasks in each one. Our results show that pictures, in general, attract attention first, before the product names and price information. Furthermore, images attract less total attention than textual information. Images attract less attention when they are not crucial for completing the task, such as when purchasing a determined product or when locating product tracking information. Younger people (less than 30) spend much less time viewing the product pictures than older age groups (50 or more). According to our results, e-retailers could improve their sites’ performance by adapting the products’ presentation to the purchase tasks and visitor characteristics.
Open Access
Differences in specific abdominal fat depots between metabolically healthy and unhealthy children with overweight/obesity: the role of cardiorespiratory fitness
(Wiley, 2023) Cadenas-Sánchez, Cristina; Medrano Echeverría, María; Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Idoate, Fernando; Osés Recalde, Maddi; Rodríguez Vigil, Beatriz; Álvarez de Eulate, Natalia; Alberdi Aldasoro, Nerea; Ortega, Francisco B.; Labayen Goñi, Idoia; Ciencias de la Salud; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Institute of Smart Cities - ISC; Osasun Zientziak; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Objectives: Fat depots localization has a critical role in the metabolic health status of adults. Nevertheless, whether that is also the case in children remains under- studied. Therefore, the aims of this study were: (i) to examine the differ-ences between metabolically healthy (MHO) and unhealthy (MUO) overweight/obesity phenotypes on specific abdominal fat depots, and (ii) to further explore whether cardiorespiratory fitness plays a major role in the differences between metabolic phenotypes among children with overweight/obesity. Methods: A total of 114 children with overweight/obesity (10.6 ±1.1 years, 62 girls) were included. Children were classified as MHO (n=68) or MUO. visceral (VAT), abdominal subcutaneous (ASAT), intermuscular abdominal (IMAAT), psoas, hepatic, pancreatic, and lumbar bone marrow adipose tissues were measured by magnetic resonance imaging. Cardiorespiratory fitness was assessed using the 20 m shuttle run test. Results: MHO children had lower VAT and ASAT contents and psoas fat fraction compared to MUO children (difference =12.4%– 25.8%, all p<0.035). MUO- unfit had more VAT and ASAT content than those MUO- fit and MHO- fit (difference =34.8%– 45.3%, all p<0.044). MUO- unfit shows also greater IMAAT fat fraction than those MUO- fit and MHO- fit peers (difference =16.4%– 13.9% respectively, all p≤0.001). In addition, MHO- unfit presented higher IMAAT fat fraction than MHO- fit (difference =13.4%, p<0.001). MUO- unfit presented higher psoas fat fraction than MHO- fit (difference =29.1%, p=0.008). Conclusions: VAT together with ASAT and psoas fat fraction, were lower in MHO than in MUO children. Further, we also observed that being fit, regardless of metabolic phenotype, has a protective role over the specific abdominal fat depots among children with overweight/obesity.
Open Access
Introducing I2Head database
(ACM (Association for Computing Machinery), 2018) Martinikorena Aranburu, Ion; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Porta Cuéllar, Sonia; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
I2Head database has been created with the aim to become an optimal reference for low cost gaze estimation. It exhibits the following outstanding characteristics: it takes into account key aspects of low resolution eye tracking technology; it combines images of users gazing at different grids of points from alternative positions with registers of user's head position and it provides calibration information of the camera and a simple 3D head model for each user. Hardware used to build the database includes a 6D magnetic sensor and a webcam. A careful calibration method between the sensor and the camera has been developed to guarantee the accuracy of the data. Different sessions have been recorded for each user including not only static head scenarios but also controlled displacements and even free head movements. The database is an outstanding framework to test both gaze estimation algorithms and head pose estimation methods.
Open Access
Advancing ASL kidney image registration: a tailored pipeline with VoxelMorph
(Springer, 2025-01-31) Oyarzun Domeño, Anne; Cia Alonso, Izaskun; Echeverría Chasco, Rebeca; Fernández Seara, María A.; Martín Moreno, Paloma L.; García Fernández, Nuria; Bastarrika, Gorka; Navallas Irujo, Javier; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
In clinical renal assessment, image registration plays a pivotal role, as patient movement during data acquisition can significantly impede image post-processing and the accurate estimation of hemodynamic parameters. This study introduces a deep learning-based image registration framework specifically for arterial spin labeling (ASL) imaging. ASL is a magnetic resonance imaging technique that modifies the longitudinal magnetization of blood perfusing the kidney using a series of radiofrequency pulses combined with slice-selective gradients. After tagging the arterial blood, label images are captured following a delay, allowing the tagged blood bolus to enter the renal tissue, while control images are acquired without tagging the arterial spins. Given that perfusion maps are generated at the pixel level by subtracting control images from label images and considering the relatively small signal intensity difference, precise alignment of these images is crucial to minimize motion artefacts and prevent significant errors in perfusion calculations. Moreover, due to the extended ASL acquisition times and the anatomical location of the kidneys, renal images are often susceptible to pulsation, peristalsis, and breathing motion. These motion-induced noises and other instabilities can adversely affect ASL imaging outcomes, making image registration essential. However, research on renal MRI registration, particularly with respect to learning-based techniques, remains limited, with even less focus on renal ASL. Our study proposes a learning-based image registration approach that builds upon VoxelMorph and introduces groupwise inference as a key enhancement. The dataset includes 2448 images of transplanted kidneys (TK) and 2456 images of healthy kidneys (HK). We compared the automatic image registration results with the widely recognized optimization method Elastix. The model’s performance was evaluated using the mean structural similarity index (MSSIM), normalized correlation coefficient (NCC), temporal signal-tonoise ratio (TSNR) of the samples, and the mean cortical signal (CSIM) in perfusion-weighted images, thereby extending the evaluation beyond traditional similarity-based metrics. Our method achieved superior image registration performance, with peak NCC (0.987 ± 0.006) and MSSIM (0.869 ± 0.048) values in the kidney region, significantly surpassing Elastix and the unregistered series (p\ 0.05) on TK and HK datasets. Regularization analysis showed that higher k values (1, 2) produced smoother deformation fields, while moderate k values (0.5, 0.9) balanced smoothness and detail, maintaining low non-positive Jacobian percentages (\1%) comparable to Elastix. Additionally, our method improved CSIM by 14.3% (2.304 ± 1.167) and TSNR by 13.1% (3.888 ± 2.170) in TK, and achieved up to 13.2% (CSIM) and 29.8% (TSNR) enhancements in HK, demonstrating robustness and improved signal quality across datasets and acquisition techniques.