| Home | [21–30] << 31 32 33 34 35 36 37 38 39 40 >> [41–50] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon | ||||
| Title | Looking at People Special Issue | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal  |
IJCV |
| Volume | 126 | Issue | 2-4 | Pages | 141-143 |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA; ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ EGJ2018 | Serial | 3093 | ||
| Permanent link to this record | |||||
| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Feedback and Surround Modulated Boundary Detection | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 126 | Issue | 12 | Pages | 1367–1380 |
| Keywords | Boundary detection; Surround modulation; Biologically-inspired vision | ||||
| Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number | Admin @ si @ AkP2018b | Serial | 2991 | ||
| Permanent link to this record | |||||
| Author | Adrien Gaidon; Antonio Lopez; Florent Perronnin | ||||
| Title | The Reasonable Effectiveness of Synthetic Visual Data | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 126 | Issue | 9 | Pages | 899–901 |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GLP2018 | Serial | 3180 | ||
| Permanent link to this record | |||||
| Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez | ||||
| Title | Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 128 | Issue | Pages | 1505–1536 | |
| Keywords | Procedural generation; Human action recognition; Synthetic data; Physics | ||||
| Abstract | Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SGC2019 | Serial | 3303 | ||
| Permanent link to this record | |||||
| Author | Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure | ||||
| Title | Slanted Stixels: A way to represent steep streets | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 127 | Issue | Pages | 1643–1658 | |
| Keywords | |||||
| Abstract | This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.118; 600.124 | Approved | no | ||
| Call Number | Admin @ si @ HSC2019 | Serial | 3304 | ||
| Permanent link to this record | |||||
| Author | Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo | ||||
| Title | CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 128 | Issue | Pages | 2763–2780 | |
| Keywords | |||||
| Abstract | First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LWM2020 | Serial | 3413 | ||
| Permanent link to this record | |||||
| Author | Yaxing Wang; Luis Herranz; Joost Van de Weijer | ||||
| Title | Mix and match networks: multi-domain alignment for unpaired image-to-image translation | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 128 | Issue | Pages | 2849–2872 | |
| Keywords | |||||
| Abstract | This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.109; 600.106; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHW2020 | Serial | 3424 | ||
| Permanent link to this record | |||||
| Author | Meysam Madadi; Hugo Bertiche; Sergio Escalera | ||||
| Title | Deep unsupervised 3D human body reconstruction from a sparse set of landmarks | Type | Journal Article | ||
| Year | 2021 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 129 | Issue | Pages | 2499–2512 | |
| Keywords | |||||
| Abstract | In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MBE2021 | Serial | 3654 | ||
| Permanent link to this record | |||||
| Author | Yaxing Wang; Abel Gonzalez-Garcia; Chenshen Wu; Luis Herranz; Fahad Shahbaz Khan; Shangling Jui; Jian Yang; Joost Van de Weijer | ||||
| Title | MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains | Type | Journal Article | ||
| Year | 2024 | Publication | International Journal of Computer Vision | Abbreviated Journal |
IJCV |
| Volume | 132 | Issue | Pages | 490–514 | |
| Keywords | |||||
| Abstract | Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP; MACO | Approved | no | ||
| Call Number | Admin @ si @ WGW2024 | Serial | 3888 | ||
| Permanent link to this record | |||||
| Author | Francesco Ciompi; Oriol Pujol; Carlo Gatta; Oriol Rodriguez-Leor; J. Mauri; Petia Radeva | ||||
| Title | Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal of Cardiovascular Imaging | Abbreviated Journal |
IJCI |
| Volume | 26 | Issue | 7 | Pages | 763–779 |
| Keywords | |||||
| Abstract | Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1569-5794 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ CPG2010 | Serial | 1305 | ||
| Permanent link to this record | |||||
| Author | Francesc Carreras; Jaume Garcia; Debora Gil; Sandra Pujadas; Chi ho Lion; R.Suarez-Arias; R.Leta; Xavier Alomar; Manuel Ballester; Guillem Pons-Llados | ||||
| Title | Left ventricular torsion and longitudinal shortening: two fundamental components of myocardial mechanics assessed by tagged cine-MRI in normal subjects | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Cardiovascular Imaging | Abbreviated Journal |
IJCI |
| Volume | 28 | Issue | 2 | Pages | 273-284 |
| Keywords | Magnetic resonance imaging (MRI); Tagging MRI; Cardiac mechanics; Ventricular torsion | ||||
| Abstract | Cardiac magnetic resonance imaging (Cardiac MRI) has become a gold standard diagnostic technique for the assessment of cardiac mechanics, allowing the non-invasive calculation of left ventric- ular long axis longitudinal shortening (LVLS) and absolute myocardial torsion (AMT) between basal and apical left ventricular slices, a movement directly related to the helicoidal anatomic disposition of the myocardial fibers. The aim of this study is to determine AMT and LVLS behaviour and normal values from a group of healthy subjects. A group of 21 healthy volunteers (15 males) (age: 23–55 y.o., mean:30.7 ± 7.5) were prospectively included in an obser- vational study by Cardiac MRI. Left ventricular rotation (degrees) was calculated by custom-made software (Harmonic Phase Flow) in consecutive LV short axis planes tagged cine-MRI sequences. AMT was determined from the difference between basal and apical planes LV rotations. LVLS (%) was determined from the LV longitudinal and horizontal axis cine-MRI images. All the 21 cases studied were interpretable, although in three cases the value of the LV apical rotation could not be determined. The mean rotation of the basal and apical planes at end-systole were -3.71° ± 0.84° and 6.73° ± 1.69° (n:18) respectively, resulting in a LV mean AMT of 10.48° ± 1.63° (n:18). End-systolic mean LVLS was 19.07 ± 2.71%. Cardiac MRI allows for the calculation of AMT and LVLS, fundamental functional components of the ventricular twist mechanics conditioned, in turn, by the anatomical helical layout of the myocardial fibers. These values provide complementary information about systolic ventricular function in relation to the traditional parameters used in daily practice. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Netherlands | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1569-5794 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ CGG2012 | Serial | 1496 | ||
| Permanent link to this record | |||||
| Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
| Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project | Type | Journal Article | ||
| Year | 2023 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal |
IJCARS |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ TGM2023 | Serial | 3830 | ||
| Permanent link to this record | |||||
| Author | Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil | ||||
| Title | A benchmark for the evaluation of computational methods for bronchoscopic navigation | Type | Journal Article | ||
| Year | 2022 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal |
IJCARS |
| Volume | 17 | Issue | 1 | Pages | |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ BSC2022 | Serial | 3832 | ||
| Permanent link to this record | |||||
| Author | Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil | ||||
| Title | Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy | Type | Journal Article | ||
| Year | 2015 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal |
IJCAR |
| Volume | 10 | Issue | 6 | Pages | 935-945 |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ SBS2015a | Serial | 2611 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Ruth Aris; Agnes Borras; Esmitt Ramirez; Rafael Sebastian; Mariano Vazquez | ||||
| Title | Influence of fiber connectivity in simulations of cardiac biomechanics | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal |
IJCAR |
| Volume | 14 | Issue | 1 | Pages | 63–72 |
| Keywords | Cardiac electromechanical simulations; Diffusion tensor imaging; Fiber connectivity | ||||
| Abstract | PURPOSE:
Personalized computational simulations of the heart could open up new improved approaches to diagnosis and surgery assistance systems. While it is fully recognized that myocardial fiber orientation is central for the construction of realistic computational models of cardiac electromechanics, the role of its overall architecture and connectivity remains unclear. Morphological studies show that the distribution of cardiac muscular fibers at the basal ring connects epicardium and endocardium. However, computational models simplify their distribution and disregard the basal loop. This work explores the influence in computational simulations of fiber distribution at different short-axis cuts. METHODS: We have used a highly parallelized computational solver to test different fiber models of ventricular muscular connectivity. We have considered two rule-based mathematical models and an own-designed method preserving basal connectivity as observed in experimental data. Simulated cardiac functional scores (rotation, torsion and longitudinal shortening) were compared to experimental healthy ranges using generalized models (rotation) and Mahalanobis distances (shortening, torsion). RESULTS: The probability of rotation was significantly lower for ruled-based models [95% CI (0.13, 0.20)] in comparison with experimental data [95% CI (0.23, 0.31)]. The Mahalanobis distance for experimental data was in the edge of the region enclosing 99% of the healthy population. CONCLUSIONS: Cardiac electromechanical simulations of the heart with fibers extracted from experimental data produce functional scores closer to healthy ranges than rule-based models disregarding architecture connectivity. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM; 600.096; 601.323; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GAB2019a | Serial | 3133 | ||
| Permanent link to this record | |||||