| |
Records |
Links |
|
Author |
Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
|
| |
Title |
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 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Enric Marti; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate; Jaume Garcia; Carme Julia; Marc Vivet |
|
| |
Title |
Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Gráficos por Computador |
Type |
Miscellaneous |
| |
Year |
2009 |
Publication |
I Congreso de Docencia Universitaria |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Aprendizaje Basado en Proyectos; Project Based Learning; Aprendizaje Cooperativo; Recursos Virtuales para el Aprendizaje Cooperativo; Moodle |
|
| |
Abstract |
Presentamos una experiencia en Aprendizaje Basado en Proyectos (ABP) realizada los últimos cuatro años en Gráficos por Computador 2, asignatura de Ingeniería Informática, de la Escuela Técnica Superior de Ingeniería (ETSE) de la Universidad Autónoma de Barcelona (UAB). Utilizamos un entorno Moodle adaptado por nosotros llamado Caronte para poder gestionar la documentación generada en ABP. Primero se presenta la asignatura, basada en dos itinerarios para cursarla: ABP y TPPE (Teoría, Problemas, Prácticas, Examen). El alumno debe escoger uno de ellos. Ambos itinerarios generan una cantidad importante de documentación (entregas de trabajos y prácticas, correcciones, ejercicios, etc.) a gestionar. En la comunicación presentamos los espacios electrónicos Moodle de ambos itinerarios. Finalmente, mostramos los resultados de encuestas realizadas a los alumnos para finalmente exponer las conclusiones de la experiencia en ABP y el uso de Moodle, así como plantear mejoras y temas de discusión. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
Vigo (Spain) |
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;ADAS; |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ MRG2009a |
Serial |
1602 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
|
| |
Title |
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 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Title |
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 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
|
| |
Title |
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging |
Type |
Conference Article |
| |
Year |
2005 |
Publication |
Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
67-74 |
|
| |
Keywords |
classification; vessel border modelling; IVUS |
|
| |
Abstract |
IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IOS Press |
Place of Publication |
Amsterdam, The Netherlands |
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 @ HGR2005c |
Serial |
1549 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil |
|
| |
Title |
Mental Workload Detection Based on EEG Analysis |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. |
Abbreviated Journal |
|
|
| |
Volume |
339 |
Issue |
|
Pages |
268-277 |
|
| |
Keywords |
Cognitive states; Mental workload; EEG analysis; Neural Networks. |
|
| |
Abstract |
The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
|
| |
Address |
Virtual; October 20-22 2021 |
|
| |
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 |
CCIA |
|
| |
Notes |
IAM; 600.139; 600.118; 600.145 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3723 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
|
| |
Title |
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
|
| |
Volume |
12 |
Issue |
5 |
Pages |
2298 |
|
| |
Keywords |
Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
|
| |
Abstract |
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
|
| |
Address |
February 2022 |
|
| |
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; ADAS; 600.139; 600.145; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HYF2022 |
Serial |
3720 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil; Aura Hernandez-Sabate; Oriol Rodriguez; Josepa Mauri; Petia Radeva |
|
| |
Title |
Statistical Strategy for Anisotropic Adventitia Modelling in IVUS |
Type |
Journal Article |
| |
Year |
2006 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
|
|
| |
Volume |
25 |
Issue |
6 |
Pages |
768-778 |
|
| |
Keywords |
Corners; T-junctions; Wavelets |
|
| |
Abstract |
Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification. |
|
| |
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;MILAB |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ GHR2006 |
Serial |
1525 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
|
| |
Title |
Decremental generalized discriminative common vectors applied to images classification |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
| |
Volume |
131 |
Issue |
|
Pages |
46-57 |
|
| |
Keywords |
Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
|
| |
Abstract |
In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing 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 |
ADAS; 600.118; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DMH2017a |
Serial |
3003 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil; Aura Hernandez-Sabate; Antoni Carol; Oriol Rodriguez; Petia Radeva |
|
| |
Title |
A Deterministic-Statistic Adventitia Detection in IVUS Images |
Type |
Conference Article |
| |
Year |
2005 |
Publication |
ESC Congress |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation |
|
| |
Abstract |
Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles. |
|
| |
Address |
Stockholm; Sweden; September 2005 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
,Sweden (EU) |
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ESC |
|
| |
Notes |
IAM;MILAB |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ RMF2005a |
Serial |
1523 |
|
| Permanent link to this record |
