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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville
Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Journal Article
Year 2017 Publication Journal of Healthcare Engineering Abbreviated Journal JHCE
Volume (down) Issue Pages 2040-2295
Keywords Colonoscopy images; Deep Learning; Semantic Segmentation
Abstract Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
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Area Expedition Conference
Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 Approved no
Call Number VBS2017b Serial 2940
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Author Carles Sanchez; Debora Gil; T. Gache; N. Koufos; Marta Diez-Ferrer; Antoni Rosell
Title SENSA: a System for Endoscopic Stenosis Assessment Type Conference Article
Year 2016 Publication 28th Conference of the international Society for Medical Innovation and Technology Abbreviated Journal
Volume (down) Issue Pages
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Abstract Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies.
Address Rotterdam; The Netherlands; October 2016
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference SMIT
Notes IAM; Approved no
Call Number Admin @ si @ SGG2016 Serial 2942
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Author Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Towards a Videobronchoscopy Localization System from Airway Centre Tracking Type Conference Article
Year 2017 Publication 12th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume (down) Issue Pages 352-359
Keywords Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation
Abstract Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations.
Address Porto; Portugal; February 2017
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Area Expedition Conference VISAPP
Notes IAM; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ SEB2017 Serial 2943
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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell
Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
Year 2017 Publication European Respiratory Journal Abbreviated Journal ERJ
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Notes IAM Approved no
Call Number Admin @ si @ DGC2017b Serial 3632
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Author Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace
Title Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis Type Conference Article
Year 2017 Publication 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
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Abstract
Address Barcelona; Spain; June 2017
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Publisher Place of Publication Editor
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Area Expedition Conference CARS
Notes MV; no menciona Approved no
Call Number Admin @ si @ ABS2017 Serial 2947
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume (down) Issue Pages
Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Address Faro; Portugal; June 2017
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Area Expedition Conference IbPRIA
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017a Serial 2953
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Author Lasse Martensson; Anders Hast; Alicia Fornes
Title Word Spotting as a Tool for Scribal Attribution Type Conference Article
Year 2017 Publication 2nd Conference of the association of Digital Humanities in the Nordic Countries Abbreviated Journal
Volume (down) Issue Pages 87-89
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Abstract
Address Gothenburg; Suecia; March 2017
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-91-88348-83-8 Medium
Area Expedition Conference DHN
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ MHF2017 Serial 2954
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title Is there a pattern of Chromosome territoriality along mice spermatogenesis? Type Conference Article
Year 2017 Publication 3rd Spanish MeioNet Meeting Abstract Book Abbreviated Journal
Volume (down) Issue Pages 55-56
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Abstract
Address Miraflores de la Sierra; Madrid; June 2017
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Area Expedition Conference MEIONET
Notes IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ Serial 2958
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title Unraveling the enigmas of chromosome territoriality during spermatogenesis Type Conference Article
Year 2017 Publication IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia Abbreviated Journal
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Address UAB; Barcelona; June 2017
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Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SBG2017b Serial 2959
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Author Xinhang Song; Shuqiang Jiang; Luis Herranz
Title Combining Models from Multiple Sources for RGB-D Scene Recognition Type Conference Article
Year 2017 Publication 26th International Joint Conference on Artificial Intelligence Abbreviated Journal
Volume (down) Issue Pages 4523-4529
Keywords Robotics and Vision; Vision and Perception
Abstract Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities.
Address Melbourne; Australia; August 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
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ISSN ISBN Medium
Area Expedition Conference IJCAI
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ SJH2017b Serial 2966
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Author Xinhang Song; Luis Herranz; Shuqiang Jiang
Title Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs Type Conference Article
Year 2017 Publication 31st AAAI Conference on Artificial Intelligence Abbreviated Journal
Volume (down) Issue Pages
Keywords RGB-D scene recognition; weakly supervised; fine tune; CNN
Abstract Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.
Address San Francisco CA; February 2017
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Area Expedition Conference AAAI
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ SHJ2017 Serial 2967
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Author Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
Title Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study Type Conference Article
Year 2017 Publication International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) Abbreviated Journal
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Abstract An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identication of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores.
Address Quebec; Canada; September 2017
Corporate Author Thesis
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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 MILAB; no proj Approved no
Call Number Admin @ si @ BCR2017 Serial 2968
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Author Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote
Title Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB Type Conference Article
Year 2017 Publication 1st International Workshop on Physics Based Vision meets Deep Learning Abbreviated Journal
Volume (down) Issue Pages
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Abstract Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However,
most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset.
Address Venice; Italy; October 2017
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Area Expedition Conference ICCV-PBDL
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ AWG2017 Serial 2969
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Author Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez
Title Occlusion Aware Hand Pose Recovery from Sequences of Depth Images Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
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Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
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Area Expedition Conference FG
Notes HUPBA; ISE; 602.143; 600.098; 600.119 Approved no
Call Number Admin @ si @ MEC2017 Serial 2970
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Author Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo
Title Image vignetting reduction via a maximization of fuzzy entropy Type Conference Article
Year 2017 Publication IEEE International Conference on Fuzzy Systems Abbreviated Journal
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Abstract In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.
Address Napoles; Italia; July 2017
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Area Expedition Conference FUZZ-IEEE
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ LMG2017 Serial 2972
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