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Author Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen edit   pdf
doi  isbn
openurl 
  Title (up) Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging Type Conference Article
  Year 2014 Publication 17th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal  
  Volume 8896 Issue Pages 231-238  
  Keywords Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging  
  Abstract Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP.
 
  Address Boston; USA; September 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-14677-5 Medium  
  Area Expedition Conference STACOM  
  Notes IAM; ADAS; 600.060; 601.145; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MKF2014 Serial 2495  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri edit   pdf
doi  openurl
  Title (up) Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction Type Journal Article
  Year 2018 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 60 Issue 4 Pages 512-524  
  Keywords  
  Abstract This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
 
  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 DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 Approved no  
  Call Number Admin @ si @ DMH2018a Serial 3062  
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Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit   pdf
doi  openurl
  Title (up) Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive 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 DAG; ADAS; 600.084; 600.118; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
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Author Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska edit   pdf
url  openurl
  Title (up) Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation Type Conference Article
  Year 2018 Publication International MICCAI Brainlesion Workshop Abbreviated Journal  
  Volume 11384 Issue Pages 393-405  
  Keywords Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution  
  Abstract In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference MICCAIW  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ PSH2018 Serial 3251  
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Author Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez edit  doi
openurl 
  Title (up) Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem Type Journal
  Year 2020 Publication Mathematics Abbreviated Journal MATH  
  Volume 20 Issue 8(9) Pages 1595  
  Keywords STEM education; Project-based learning; Coding; software tool  
  Abstract In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view.
 
  Address September 2020  
  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; ISE Approved no  
  Call Number Admin @ si @ Serial 3722  
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Author Aura Hernandez-Sabate; Debora Gil; Albert Teis edit   pdf
doi  openurl
  Title (up) How Do Conservation Laws Define a Motion Suppression Score in In-Vivo Ivus Sequences? Type Conference Article
  Year 2007 Publication Proc. IEEE Ultrasonics Symp Abbreviated Journal  
  Volume Issue Pages 2231-2234  
  Keywords validation standards; IVUS motion compensation; conservation laws.  
  Abstract Evaluation of arterial tissue biomechanics for diagnosis and treatment of cardiovascular diseases is an active research field in the biomedical imaging processing area. IntraVascular UltraSound (IVUS) is a unique tool for such assessment since it reflects tissue morphology and deformation. A proper quantification and visualization of both properties is hindered by vessel structures misalignments introduced by cardiac dynamics. This has encouraged development of IVUS motion compensation techniques. However, there is a lack of an objective evaluation of motion reduction ensuring a reliable clinical application This work reports a novel score, the Conservation of Density Rate (CDR), for validation of motion compensation in in-vivo pullbacks. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; while results in in vivo pullbacks show its reliability in clinical cases.  
  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 IAM @ iam @ HTG2007 Serial 1550  
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Author Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua edit  openurl
  Title (up) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST  
  Abstract Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing 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 Approved no  
  Call Number Admin @ si @ BRJ2018 Serial 3252  
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Author Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti edit   pdf
doi  openurl
  Title (up) Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences Type Journal Article
  Year 2011 Publication IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control Abbreviated Journal T-UFFC  
  Volume 58 Issue 1 Pages 60-72  
  Keywords 3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging  
  Abstract Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals.  
  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 0885-3010 ISBN Medium  
  Area Expedition Conference  
  Notes IAM;ADAS Approved no  
  Call Number IAM @ iam @ HGG2011 Serial 1546  
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Author Aura Hernandez-Sabate; David Rotger; Debora Gil edit   pdf
doi  openurl
  Title (up) Image-based ECG sampling of IVUS sequences Type Conference Article
  Year 2008 Publication Proc. IEEE Ultrasonics Symp. IUS 2008 Abbreviated Journal  
  Volume Issue Pages 1330-1333  
  Keywords Longitudinal Motion; Image-based ECG-gating; Fourier analysis  
  Abstract Longitudinal motion artifacts in IntraVascular UltraSound (IVUS) sequences hinders a properly 3D reconstruction and vessel measurements. Most of current techniques base on the ECG signal to obtain a gated pullback without the longitudinal artifact by using a specific hardware or the ECG signal itself. The potential of IVUS images processing for phase retrieval still remains little explored. In this paper, we present a fast forward image-based algorithm to approach ECG sampling. Inspired on the fact that maximum and minimum lumen areas are related to end-systole and end-diastole, our cardiac phase retrieval is based on the analysis of tissue density of mass along the sequence. The comparison between automatic and manual phase retrieval (0.07 ± 0.07 mm. of error) encourages a deep validation contrasting with ECG signals.  
  Address Beijing (China)  
  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;MILAB Approved no  
  Call Number IAM @ iam @ HRG2008 Serial 1553  
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Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title (up) Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
 
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
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