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Your search keyword '"Quentin Angermann"' showing total 6 results
Start Over Author "Quentin Angermann" Topic [info.info-ti]computer science [cs]/image processing [eess.iv]
6 results on '"Quentin Angermann"'

1. GTCreator: a flexible annotation tool for image-based datasets

Author

Xavier Dray, Maroua Hammami, Aymeric Histace, Gloria Fernández-Esparrach, Jorge Bernal, Cristina Sánchez-Montes, Olivier Romain, Cristina Rodríguez de Miguel, Quentin Angermann, F. Javier Sánchez, Henry Córdova, Ana García-Rodríguez, Marc Masana, Universitat Autònoma de Barcelona (UAB), Computer Vision Center (Centre de visio per computador) (CVC), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), ASTRE [Cergy-Pontoise], Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Barcelona Centre for International Health Research, Hospital Clinic (CRESIB), Universitat de Barcelona (UB), CHU Saint-Antoine [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)

Subjects
Decision support system, Speedup, Computer science, 0206 medical engineering, Biomedical Engineering, Datasets as Topic, Health Informatics, 02 engineering and technology, Health informatics, 030218 nuclear medicine & medical imaging, Task (project management), 03 medical and health sciences, Annotation, 0302 clinical medicine, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, ComputingMilieux_MISCELLANEOUS, Data Curation, Flexibility (engineering), Information retrieval, business.industry, Intestinal Polyps, Usability, General Medicine, Colonoscopy, 020601 biomedical engineering, Computer Graphics and Computer-Aided Design, Computer Science Applications, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Benchmark (computing), Surgery, Computer Vision and Pattern Recognition, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract

Methodology evaluation for decision support systems for health is a time-consuming task. To assess performance of polyp detection methods in colonoscopy videos, clinicians have to deal with the annotation of thousands of images. Current existing tools could be improved in terms of flexibility and ease of use. We introduce GTCreator, a flexible annotation tool for providing image and text annotations to image-based datasets. It keeps the main basic functionalities of other similar tools while extending other capabilities such as allowing multiple annotators to work simultaneously on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. The comparison with other similar tools shows that GTCreator allows to obtain fast and precise annotation of image datasets, being the only one which offers full annotation editing and browsing capabilites. Our proposed annotation tool has been proven to be efficient for large image dataset annotation, as well as showing potential of use in other stages of method evaluation such as experimental setup or results analysis.

Published
2018
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2. Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis

Author

Olivier Romain, Quentin Angermann, Maroua Hammami, Gloria Fernández-Esparrach, Aymeric Histace, Xavier Dray, F. Javier Sánchez, Cristina Sánchez-Montes, Jorge Bernal, ASTRE [Cergy-Pontoise], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Universitat Autònoma de Barcelona (UAB), Barcelona Centre for International Health Research, Hospital Clinic (CRESIB), Universitat de Barcelona (UB), SATT IdF, Springer, iPolyp, GIANA, and CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)

Subjects
Image quality, Stability (learning theory), real-time, Colonoscopy, [SDV.CAN]Life Sciences [q-bio]/Cancer, Still frame, Machine learning, computer.software_genre, Polyp detection, 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, colonoscopy, 0103 physical sciences, Medicine, Adaptation (computer science), Simulation, medicine.diagnostic_test, business.industry, Video sequence, Gold standard (test), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Key (cryptography), spatiotemporal coherence, 030211 gastroenterology & hepatology, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer
Abstract

International audience; Colorectal cancer is the second cause of cancer death inUnited States: precursor lesions (polyps) detection is key for patientsurvival. Though colonoscopy is the gold standard screening tool, somepolyps are still missed. Several computational systems have been pro-posed but none of them are used in the clinical room mainly due to com-putational constraints. Besides, most of them are built over still framedatabases, decreasing their performance on video analysis due to the lackof output stability and not coping with associated variability on imagequality and polyp appearance. We propose a strategy to adapt thesemethods to video analysis by adding a spatio-temporal stability mod-ule and studying a combination of features to capture polyp appearancevariability. We validate our strategy, incorporated on a real-time detec-tion method, on a public video database. Resulting method detects allpolyps under real time constraints, increasing its performance due to ouradaptation strategy.

Published
2017
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3. Embeddable Real Time Tool For Automatic Skin Lesions Characterization

Author

Quentin Angermann, Olivier Romain, Maroua Hammami, Aymeric Histace, Histace, Aymeric, ASTRE [Cergy-Pontoise], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), and Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)

Subjects
Engineering, Image processing, 02 engineering and technology, [INFO] Computer Science [cs], 030218 nuclear medicine & medical imaging, Smart Embedded System, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO]Computer Science [cs], Computer vision, Skin Cancer, business.industry, Computer-Aided-Adiagnosis, 020206 networking & telecommunications, medicine.disease, 3. Good health, Characterization (materials science), [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, Skin cancer, Skin lesion, business, Biomedical engineering
Abstract

International audience; Skin cancer is one of the most diagnosed cancer in the world. This cancer is based on two kinds of lesions:carcinomas and melanomas. Both are related to the degeneration of nevi. In this abstract, we aim to propose a tool to help people and physicians to determine automatically if a nevuscould be dangerous or not. To be accessible to the general public, such system must be embeddable at thehighest possible level, like a smartphone application.

Published
2017

4. Smart Videocapsule for Early Diagnosis of Colorectal Cancer: Toward Embedded Image Analysis

Author

Quentin Angermann, Aymeric Histace, Andrea Pinna, Bertrand Granado, Xavier Dray, Olivier Romain, ASTRE [Cergy-Pontoise], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), Hôpital Lariboisière-APHP, Systèmes Electroniques (SYEL), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Springer, and Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)

Subjects
Boosting (machine learning), Image processing, 02 engineering and technology, Hough transform, law.invention, Image (mathematics), [SPI]Engineering Sciences [physics], law, Capsule endoscopy, Technical Presentation, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Polyp Localization, Field-programmable gate array, Simulation, Colorectal Cancer, business.industry, Energy performance, 3. Good health, 020202 computer hardware & architecture, [SPI.TRON]Engineering Sciences [physics]/Electronics, Videocapsule, Embedded system, Embedded Detection, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Energy Performance, 020201 artificial intelligence & image processing, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; For the last 20 years, wireless videocapsule technology has triggered alot of interest in the gastroenterologist community for the non-invasive early detectionof various gastrointestinal pathologies (ulcers, Chrones disease, polyp detection,etc.). Nevertheless, in most of the European countries videocapsules are notyet considered as a systematic valid alternative to classic endoscopies and colonoscopies.Main reasons are in the existing technological limitations of videocapsulesthat are of two kinds: (i) A limited battery life-time (8 hours usually ensured bythe manufacturer) that does not allow a complete imaging of the gastro intestinaltract, and (ii) the limited performance of the device in terms of detection rate of particularstructures like polyps for instance which degenerations are at the origin ofcolorectal cancer. To overpass these limitations, main idea of our work is to developa generation of smart videocapsules that takes advantage of the constant progress inelectronics and most precisely in embedded signal processing tasks. In this Chapter,we give first a detailed overview of the most recent state-of-the-art related tovideocapsules from the technological perspective in order to clearly positioned ourwork among the existing products and on going projects. In a second time, we proposea synthetic recall of the Cyclope project in the framework of which we arestudying different strategies to improve the performance of current videocapsules inthe particular context of the early diagnosis of colorectal cancer (polyp detection).We then propose a particular focus on the design optimization of the proposed algorithms from an electronic perspective. Most precisely, we give concrete elementsand quantitative estimation (time processing, embedding performance, etc.) to showthat embedding of the signal processing IP inside the videocapsule is feasible consideringthe most recent FPGA-platform performance, and that such an integrationcan bring a positive balance in terms of energy consumption by drastically reducingthe amount of transmitted data.

Published
2015
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5. Active Learning for Real Time Detection of Polyps in Videocolonoscopy

Author

Quentin Angermann, Olivier Romain, Aymeric Histace, ASTRE [Cergy-Pontoise], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), 'Smart Videocolonoscopy' project (SATT IdFInnov), British Machine Vision Association, and Elsevier

Subjects
Colorectal cancer, Active learning (machine learning), Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Active Learning, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, Computer Aided Detection, Real-Time Analysis, 03 medical and health sciences, 0302 clinical medicine, Videocolonoscopy, medicine, General Environmental Science, Colorectal Cancer, business.industry, Frame (networking), medicine.disease, Computer aided detection, Constraint (information theory), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], General Earth and Planetary Sciences, 030211 gastroenterology & hepatology, Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

In this paper a method to perform real-time detection of polyps in videocolonoscopy is introduced. Polyps are at the origins of colorectal cancer which is one of the deadliest diseases in the world. Many methods to improve detection of polyps have been proposed so far. However performance of these methods strongly depends on the available computational resources and, until now, are not able to perform real-time detection during a standard examination. The proposed method, based on active learning, is able to solve these issues. Most precisely, this approach allows us to detect approximately 90% of polyps on a freely available database introduced to the community in 2012, for a F2 score of 65%, and matches real-time constraint by making possible the analysis of a frame in only 0.023s (average value) on a standard computer not necessarily dedicated to that kind of application.

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6. Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge

Author

Gloria Fernández-Esparrach, Konstantin Pogorelov, Bogdan J. Matuszewski, Quentin Angermann, Suryakanth R. Gurudu, Francisco Javier Sánchez, Patrick Brandao, Danail Stoyanov, Lequan Yu, Henry Córdova, Aymeric Histace, Cristina Sánchez-Montes, Ilangko Balasingham, Lena Maier-Hein, Nima Tajkbaksh, Sungbin Choi, Jianming Liang, Olivier Romain, Xavier Dray, Jorge Bernal, Stefanie Speidel, Quentin Debard, Hao Chen, Bjorn Rustad, Universitat Autònoma de Barcelona (UAB), Arizona State University [Tempe] (ASU), University of Central Lancashire [Preston] (UCLAN), Wuhan University [China], ASTRE [Cergy-Pontoise], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), University of Oslo (UiO), Oslo University Hospital [Oslo], Department of Electronics and Telecommunication, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU), SIMULA, Chung-Ang University [Seoul], Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Karlsruhe Institute of Technology (KIT), University College of London [London] (UCL), Barcelona Centre for International Health Research, Hospital Clinic (CRESIB), Universitat de Barcelona (UB), Mayo Clinic, This work was supported by several grants through ASU-Mayo Clinic partnerships, by the Spanish Government through the funded project iVENDIS (DPI2015-65286-R), by the FSEED and by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, 2014-SGR-1470 and2014-SGR-135. The authors would further like to acknowledge support from the European Union through the ERC starting grant COMBIOSCOPY under the New Horizon Framework Programme grant agreement ERC-2015-StG-37960. Finally, authors thank SATT IdfInnov for their support through the 'iPolyp' project (Number 186)., and Universitat de Barcelona

Subjects
I430, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Endoscopic vision, MEDLINE, Colonic Polyps, Colonoscopy, Hand- crafted features, Machine learning, computer.software_genre, Convolutional neural network, 030218 nuclear medicine & medical imaging, B800, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Càncer colorectal, Computer-Assisted Intervention, Medical imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Medicine, Computer vision, Electrical and Electronic Engineering, Endoscòpia, Early Detection of Cancer, Polyp Detection, Radiological and Ultrasound Technology, medicine.diagnostic_test, I440, business.industry, Early disease, Medical image computing, Colonoscòpia, Endoscopy, Gold standard (test), Colorectal cancer, 3. Good health, Computer Science Applications, Validation Framework, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Colonic Neoplasms, 030211 gastroenterology & hepatology, Neural Networks, Computer, Artificial intelligence, business, computer, I460, Software
Abstract

International audience; Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.

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