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1. Computer-aided diagnosis improves characterization of Barrett’s neoplasia by general endoscopists (with video)

Author

Alkhalaf, Alaa, Herrero, Lorenza Alvarez, Baldaque-Silva, Francisco, Barret, Maximilien, Bergman, Jacques J., Beyna, Torsten, Bisschops, Raf, Boers, Tim G., Curvers, Wouter, Deprez, Pierre H., Duits, Lucas C., Elbe, Peter, Esteban, Jose Miguel, Falk, Gary W., Fockens, Kiki N., Ginsberg, Gregory G., de Groof, Albert J., Haidry, Rehan, Houben, Martin H., Infantolino, Anthony, Iyer, Prasad G., Jong, Martijn, Jonge, Pieter-Jan de, Jukema, Jelmer B., Koch, Arjun, Komanduri, Srinadh, Konda, Vani, Kusters, Koen C., Leclerq, Philippe, Leggett, Cadman L., Lightdale, Charles J., Mallant-Hent, Rosalie C., Villarejo, Guiomar Moral, Fernández-Sordo, Jacobo Ortiz, Pech, Oliver, Penman, Ian, Pouw, Roos E., van der Putten, Joost A., Ragunath, Krish, Scholten, Pieter, Seewald, Stefan, Sethi, Amritha, Smith, Michael S., van der Sommen, Fons, Trindade, Arvind, Wani, Sachin, Waxman, Irving, Westerhof, Jessie, Weusten, Bas L., de With, Peter H., Wolfsen, Herbert C., Kusters, Carolus H.J., Jong, Martijn R., Boers, Tim, Weusten, Bas L.A.M., Houben, Martin H.M.G., Nagengast, Wouter B., Mallant-Hent, Rosalie, Silva, Francisco Baldaque, Ortiz Fernández-Sordo, Jacobo, Moral Villarejo, Guiomar, Montazeri, Nahid S.M., der Sommen, Fons van, and de Groof, A. Jeroen

Published
2024
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2. The use of a real-time computer-aided detection system for visible lesions in the Barrett’s esophagus during live endoscopic procedures: a pilot study (with video)

Author

Alkhalaf, Alaa, Herrero, Lorenza Alvarez, Baldaque-Silva, Francisco, Barret, Maximilien, Bergman, Jacques J., Beyna, Torsten, Boers, Tim G., Elbe, Peter, Fockens, Kiki N., de Groof, Albert J., Houben, Martin H., Jong, Martijn, Jukema, Jelmer B., Kusters, Koen C., Mallant-Hent, Rosalie C., Villarejo, Guiomar Moral, Nagengast, Wouter, Fernández-Sordo, Jacobo Ortiz, Pech, Oliver, Pouw, Roos E., van der Putten, Joost A., Ragunath, Krish, Scholten, Pieter, Seewald, Stefan, der Sommen, Fons van, Westerhof, Jessie, Weusten, Bas L., de With, Peter H., Jong, Martijn R., Boers, Tim, Kusters, Carolus H.J., Duits, Lucas C., van der Sommen, Fons, and de Groof, A. Jeroen

Published
2024
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6. Proceedings of Workshop AEW10: Concepts in Information Theory and Communications

Author

Immink, Kees A. Schouhamer, Baggen, Stan, Chaabane, Ferdaous, Chen, Yanling, de With, Peter H. N., Gassara, Hela, Gharbi, Hamed, Ghazel, Adel, Grati, Khaled, Grigoryan, Naira M., Harutyunyan, Ashot, Imanishi, Masayuki, Iwamoto, Mitsugu, Iwata, Ken-ichi, Kamabe, Hiroshi, Kurkoski, Brian M., Kuzuoka, Shigeaki, Langenhuizen, Patrick, Lewandowsky, Jan, Manada, Akiko, Miyake, Shigeki, Morita, Hiroyoshi, Muramatsu, Jun, Najjar, Safa, Poghosyan, Arnak V., Rouissi, Fatma, Sakai, Yuta, Tamm, Ulrich, van der Putten, Joost, van der Sommen, Fons, Vinck, A. J. Han, Wadayama, Tadashi, Wübben, Dirk, and Yamamoto, Hirosuke

Subjects
Computer Science - Information Theory, 68P30, 94A05
Abstract

The 10th Asia-Europe workshop in "Concepts in Information Theory and Communications" AEW10 was held in Boppard, Germany on June 21-23, 2017. It is based on a longstanding cooperation between Asian and European scientists. The first workshop was held in Eindhoven, the Netherlands in 1989. The idea of the workshop is threefold: 1) to improve the communication between the scientist in the different parts of the world; 2) to exchange knowledge and ideas; and 3) to pay a tribute to a well respected and special scientist., Comment: 44 pages, editors for the proceedings: Yanling Chen and A. J. Han Vinck

Published
2017

7. Computer-aided diagnosis improves characterization of Barrett's neoplasia by general endoscopists (with video).

Author

Jukema, Jelmer B., Kusters, Carolus H.J., Jong, Martijn R., Fockens, Kiki N., Boers, Tim, van der Putten, Joost A., Pouw, Roos E., Duits, Lucas C., Weusten, Bas L.A.M., Herrero, Lorenza Alvarez, Houben, Martin H.M.G., Nagengast, Wouter B., Westerhof, Jessie, Alkhalaf, Alaa, Mallant-Hent, Rosalie, Scholten, Pieter, Ragunath, Krish, Seewald, Stefan, Elbe, Peter, and Silva, Francisco Baldaque

Abstract

Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia. The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance. Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P =.04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P <.001). CADx assistance increased endoscopists' confidence in characterization (P <.001). CADx performance was similar to that of the BE experts. CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance. [ABSTRACT FROM AUTHOR]

Published
2024
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9. The use of a real-time computer-aided detection system for visible lesions in the Barrett’s esophagus during live endoscopic procedures, a pilot study

Author

Fockens, Kiki N., primary, Jukema, Jelmer B., additional, Jong, Martijn R., additional, Boers, Tim, additional, van der Putten, Joost A., additional, Kusters, Carolus H.J., additional, Pouw, Roos E., additional, Duits, Lucas C., additional, Sommen, Fons van der, additional, de With, Peter H., additional, de Groof, A. Jeroen, additional, and Bergman, Jacques J., additional

Published
2024
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10. The use of a real-time computer-aided detection system for visible lesions in the Barrett's esophagus during live endoscopic procedures: a pilot study (with video)

Author

MS MDL 1, Cancer, Fockens, Kiki N., Jukema, Jelmer B., Jong, Martijn, Pouw, Roos E., Duits, Lucas C., de Groof, Albert J., Bergman, Jacques J., Boers, Tim G., van der Putten, Joost A., Kusters, Carolus H.J., der Sommen, Fons van, de With, Peter H., Alkhalaf, Alaa, Herrero, Lorenza Alvarez, Baldaque-Silva, Francisco, Barret, Maximilien, Weusten, Bas L., BONS-AI consortium, MS MDL 1, Cancer, Fockens, Kiki N., Jukema, Jelmer B., Jong, Martijn, Pouw, Roos E., Duits, Lucas C., de Groof, Albert J., Bergman, Jacques J., Boers, Tim G., van der Putten, Joost A., Kusters, Carolus H.J., der Sommen, Fons van, de With, Peter H., Alkhalaf, Alaa, Herrero, Lorenza Alvarez, Baldaque-Silva, Francisco, Barret, Maximilien, Weusten, Bas L., and BONS-AI consortium

Published
2024

11. Pseudo-labeled Bootstrapping and Multi-stage Transfer Learning for the Classification and Localization of Dysplasia in Barrett’s Esophagus

Author

van der Putten, Joost, de Groof, Jeroen, van der Sommen, Fons, Struyvenberg, Maarten, Zinger, Svitlana, Curvers, Wouter, Schoon, Erik, Bergman, Jacques, de With, Peter H. N., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Suk, Heung-Il, editor, Liu, Mingxia, editor, Yan, Pingkun, editor, and Lian, Chunfeng, editor

Published
2019
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15. A deep learning system for detection of early Barrett's neoplasia: a model development and validation study

Author

Fockens, K N, primary, Jong, M R, additional, Jukema, J B, additional, Boers, T G W, additional, Kusters, C H J, additional, van der Putten, J A, additional, Pouw, R E, additional, Duits, L C, additional, Montazeri, N S M, additional, van Munster, S N, additional, Weusten, B L A M, additional, Alvarez Herrero, L, additional, Houben, M H M G, additional, Nagengast, W B, additional, Westerhof, J, additional, Alkhalaf, A, additional, Mallant-Hent, R C, additional, Scholten, P, additional, Ragunath, K, additional, Seewald, S, additional, Elbe, P, additional, Baldaque-Silva, F, additional, Barret, M, additional, Ortiz Fernández-Sordo, J, additional, Villarejo, G Moral, additional, Pech, O, additional, Beyna, T, additional, van der Sommen, F, additional, de With, P H, additional, de Groof, A J, additional, Bergman, J J, additional, Alkhalaf, Alaa, additional, Alvarez Herrero, Lorenza, additional, Baldaque-Silva, Francisco, additional, Barret, Maximilien, additional, Bergman, Jacques J, additional, Beyna, Torsten, additional, Bisschops, Raf, additional, Boers, Tim G, additional, Curvers, Wouter, additional, Deprez, Pierre H, additional, Duits, Lucas C, additional, Elbe, Peter, additional, Esteban, Jose M, additional, Falk, Gary W, additional, Fockens, Kiki N, additional, Ganguly, Eric, additional, Ginsberg, Gregory G, additional, de Groof, Albert J, additional, Haidry, Rehan, additional, Houben, Martin H, additional, Infantolino, Anthony, additional, Iyer, Prasad G, additional, Jong, Martijn R, additional, De Jonge, Pieter-Jan, additional, Jukema, Jelmer B, additional, Koch, Arjun K, additional, Komanduri, Srinadh, additional, Konda, Vani, additional, Kusters, Carolus H J, additional, Leclercq, Philippe, additional, Leggett, Cadman L, additional, Lemmers, Arnaud, additional, Lightdale, Charles J, additional, Mallant-Hent, Rosalie C, additional, Moral Villarejo, Guiomar, additional, Muthusamy, V Raman, additional, Nagengast, Wouter, additional, Ortiz Fernández-Sordo, Jacobo, additional, Pech, Oliver, additional, Penman, Ian, additional, Pleskow, Douglas K, additional, Pouw, Roos E, additional, van der Putten, Joost A, additional, Ragunath, Krish, additional, Scholten, Pieter, additional, Seewald, Stefan, additional, Sethi, Amritha, additional, Smith, Michael S, additional, Van der Sommen, Fons, additional, Trindade, Arvind, additional, Wani, Sachin, additional, Waxman, Irving, additional, Westerhof, Jessie, additional, Weusten, Bas L, additional, de With, Peter H N, additional, and Wolfsen, Herbert C, additional

Published
2023
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17. COMPUTER-AIDED DIAGNOSIS (CADX) IMPROVES CHARACTERIZATION OF BARRETT’S NEOPLASIA BY ENDOSCOPISTS

Author

Jukema, Jelmer, primary, Kusters, Koen, additional, Jong, Martijn, additional, Fockens, Kiki, additional, Boers, Tim, additional, Van Der Putten, Joost, additional, Pouw, Roos, additional, Duits, Lucas, additional, Weusten, Bas, additional, Herrero, L. Alvarez, additional, Houben, Martin, additional, Nagengast, Wouter, additional, Westerhof, Jessie, additional, Alkhalaf, A., additional, Mallant-Hent, Rosalie, additional, Scholten, Pieter, additional, Ragunath, Krish, additional, Seewald, Stefan, additional, Elbe, Peter, additional, Baldaque-Silva, Francisco, additional, Barret, Maximilien, additional, Fernández-Sordo, Jacobo Ortiz, additional, Villarejo, Guiomar Moral, additional, Pech, Oliver, additional, Beyna, Torsten, additional, Van Der Sommen, Fons, additional, De With, P.H.N., additional, De Groof, Jeroen, additional, and Bergman, Jacques, additional

Published
2023
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18. VIDEO-BASED COMPUTER AIDED DETECTION SYSTEM IMPROVES BARRETT’S NEOPLASIA DETECTION OF GENERAL ENDOSCOPISTS IN A MULTI-STEP BENCHMARKING STUDY

Author

Jong, Martijn, primary, Fockens, Kiki, additional, Jukema, Jelmer, additional, Boers, Tim, additional, Kusters, Koen, additional, Van Der Putten, Joost, additional, Pouw, Roos, additional, Duits, Lucas, additional, Weusten, Bas, additional, Herrero, L. Alvarez, additional, Houben, Martin, additional, Nagengast, Wouter, additional, Westerhof, Jessie, additional, Alkhalaf, A., additional, Mallant-Hent, Rosalie, additional, Scholten, Pieter, additional, Ragunath, Krish, additional, Seewald, Stefan, additional, Elbe, Peter, additional, Baldaque-Silva, Francisco, additional, Barret, Maximilien, additional, Fernández-Sordo, Jacobo Ortiz, additional, Villarejo, Guiomar Moral, additional, Pech, Oliver, additional, Beyna, Torsten, additional, Van Der Sommen, Fons, additional, De With, P.H.N., additional, De Groof, Jeroen, additional, and Bergman, Jacques, additional

Published
2023
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20. Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set

Author

Fockens, Kiki N., primary, Jukema, Jelmer B., additional, Boers, Tim, additional, Jong, Martijn R., additional, van der Putten, Joost A., additional, Pouw, Roos E., additional, Weusten, Bas L. A. M., additional, Alvarez Herrero, Lorenza, additional, Houben, Martin H. M. G., additional, Nagengast, Wouter B., additional, Westerhof, Jessie, additional, Alkhalaf, Alaa, additional, Mallant, Rosalie, additional, Ragunath, Krish, additional, Seewald, Stefan, additional, Elbe, Peter, additional, Barret, Maximilien, additional, Ortiz Fernández‐Sordo, Jacobo, additional, Pech, Oliver, additional, Beyna, Torsten, additional, van der Sommen, Fons, additional, de With, Peter H., additional, de Groof, A. Jeroen, additional, and Bergman, Jacques J., additional

Published
2023
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22. Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set

Author

MS MDL 1, Cancer, Fockens, Kiki N., Jukema, Jelmer B., Boers, Tim, Jong, Martijn R., van der Putten, Joost A., Pouw, Roos E., Weusten, Bas L.A.M., Alvarez Herrero, Lorenza, Houben, Martin H.M.G., Nagengast, Wouter B., Westerhof, Jessie, Alkhalaf, Alaa, Mallant, Rosalie, Ragunath, Krish, Seewald, Stefan, Elbe, Peter, Barret, Maximilien, Ortiz Fernández-Sordo, Jacobo, Pech, Oliver, Beyna, Torsten, van der Sommen, Fons, de With, Peter H., de Groof, A. Jeroen, Bergman, Jacques J., MS MDL 1, Cancer, Fockens, Kiki N., Jukema, Jelmer B., Boers, Tim, Jong, Martijn R., van der Putten, Joost A., Pouw, Roos E., Weusten, Bas L.A.M., Alvarez Herrero, Lorenza, Houben, Martin H.M.G., Nagengast, Wouter B., Westerhof, Jessie, Alkhalaf, Alaa, Mallant, Rosalie, Ragunath, Krish, Seewald, Stefan, Elbe, Peter, Barret, Maximilien, Ortiz Fernández-Sordo, Jacobo, Pech, Oliver, Beyna, Torsten, van der Sommen, Fons, de With, Peter H., de Groof, A. Jeroen, and Bergman, Jacques J.

Published
2023

23. A ROBUST AND COMPACT DEEP LEARNING SYSTEM FOR PRIMARY DETECTION OF EARLY BARRETT’S NEOPLASIA OUTPERFORMS GENERAL ENDOSCOPISTS

Author

Fockens, Kiki, primary, Jukema, Jelmer, additional, Jong, Martijn, additional, Boers, Tim G., additional, Van Der Putten, Joost, additional, Pouw, Roos E., additional, Weusten, Bas L., additional, Houben, Martin H., additional, Nagengast, Wouter B., additional, Westerhof, Jessie, additional, Herrero, L. Alvarez, additional, Alkhalaf, A., additional, Ragunath, Krish, additional, Barret, Maximilien, additional, Ortiz-Fernandez-Sordo, Jacobo, additional, Pech, Oliver, additional, Beyna, Torsten, additional, Seewald, Stefan, additional, Van Der Sommen, Fons, additional, De With, P.H.N., additional, De Groof, Jeroen, additional, and Bergman, Jacques, additional

Published
2022
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24. Efficient endoscopic frame informativeness assessment by reusing the encoder of the primary CAD task

Author

Mammadli, Fidan, van der Sommen, Fons, Boers, Tim, van der Putten, Joost, Fockens, Kiki N., Jukema, Jelmer B., de Jong, Martijn R., Bergman, Jacques J. G. H. M., de With, Peter H. N., Drukker, Karen, Iftekharuddin, Khan M., Gastroenterology and Hepatology, Graduate School, CCA - Imaging and biomarkers, and Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
Image Quality, Barrett's esophagus, Frame Informativeness Assessment, CAD, Endoscopy
Abstract

The majority of the encouraging experimental results published on AI-based endoscopic Computer-Aided Detection (CAD) systems have not yet been reproduced in clinical settings, mainly due to highly curated datasets used throughout the experimental phase of the research. In a realistic clinical environment, these necessary high image-quality standards cannot be guaranteed, and the CAD system performance may degrade. While several studies have previously presented impressive outcomes with Frame Informativeness Assessment (FIA) algorithms, the current-state of the art implies sequential use of FIA and CAD systems, affecting the time performance of both algorithms. Since these algorithms are often trained on similar datasets, we hypothesise that part of the learned feature representations can be leveraged for both systems, enabling a more efficient implementation. This paper explores this case for early Barrett cancer detection by integrating the FIA algorithm within the CAD system. Sharing the weights between two tasks reduces the number of parameters from 16 to 11 million and the number of floating-point operations from 502 to 452 million. Due to the lower complexity of the architecture, the proposed model leads to inference time up to 2 times faster than the state-of-The-Art sequential implementation while retaining the classification performance.

Published
2022

26. ID: 3525469 IDENTIFICATION OF DYSPLASIA IN THE BARRETT'S ESOPHAGUS USING AN ENDOCYTOSCOPY CLASSIFICATION SYSTEM: PRELIMINARY RESULTS OF A PROSPECTIVE COMPARISON BETWEEN CLINICIANS AND ARTIFICIAL INTELLIGENCE

Author

van der Laan, Jouke J., primary, Van Der Putten, Joost, additional, Zhao, Xiaojuan, additional, Schmidt, Iris, additional, Gabriëls, Ruben Y., additional, Karrenbeld, Arend, additional, Peters, Frans, additional, Westerhof, Jessie, additional, Van Der Sommen, Fons, additional, and Nagengast, Wouter B., additional

Published
2021
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27. Fast tissue detection in volumetric laser endomicroscopy using convolutional neural networks: an object-detection approach

Author

Selmanaj, Endi, van der Sommen, Fons, Okel, Sanne E., van der Putten, Joost, Struyvenberg, Maarten R., Bergman, Jacques J. G. H. M., de With, Peter H. N., Isgum, Ivana, Landman, Bennett A., Gastroenterology and Hepatology, Graduate School, CCA - Imaging and biomarkers, CCA - Cancer Treatment and Quality of Life, Amsterdam Gastroenterology Endocrinology Metabolism, Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Eindhoven MedTech Innovation Center, and EAISI Health

Subjects
Pixel, Object detection, Computer science, business.industry, Deep learning, CAD, SDG 3 – Goede gezondheid en welzijn, Barrett's Esophagus, Convolutional neural network, Rendering (computer graphics), Segmentation, SDG 3 - Good Health and Well-being, Endomicroscopy, Computer vision, Artificial intelligence, business
Abstract

Barrett's Esophagus is a precursor of esophageal adenocarcinoma, one of the most lethal forms of cancer. Volumetric laser endomicroscopy (VLE) is a relatively new technology used for early detection of abnormal cells in BE by imaging the inner tissue layers of the esophagus. Computer-Aided Detection (CAD) shows great promise in analyzing the VLE frames due to the advances in deep learning. However, a full VLE scan produces 1,200 scans of 4,096 x 2,048 pixels, making automated pre-processing for the tissue of interest extraction necessary. This paper explores an object detection for tissue detection in VLE scans. We show that this can be achieved in real time with very low inference time, using single-stage object detection like YOLO. Our best performing model achieves a value of 98.23% for the mean average precision of bounding boxes correctly predicting the tissue of interest. Additionally, we have found that the tiny YOLO with Partial Residual Networks architecture further reduces the inference speed with a factor of 10, while only sacrificing less than 1% of accuracy. This proposed method does not only segment the tissue of interest in real time without any latency, but it can also achieve this efficiently using limited GPU resources, rendering it attractive for embedded applications. Our paper is the first to introduce object detection as a new approach for VLE-data tissue segmentation and paves the way for real-time VLE-based detection of early cancer in BE.

Published
2021

28. Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks

Author

Okel, Sanne E., van der Sommen, Fons, Selmanaj, Endi, van der Putten, Joost, Struyvenberg, Maarten R., Bergman, Jacques J.G.H.M., De With, Peter H.N., Mazurowski, Maciej A., Drukker, Karen, Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Eindhoven MedTech Innovation Center, EAISI Health, Gastroenterology and Hepatology, Graduate School, CCA - Imaging and biomarkers, CCA - Cancer Treatment and Quality of Life, and Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
Floating point, Tissue segmentation, Computer science, business.industry, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Recurrent neural network, Boundary (topology), CAD, Computer aided detection, SDG 3 – Goede gezondheid en welzijn, Laser, law.invention, Volumetric laser endomicroscopy, Barrett's esophagus, SDG 3 - Good Health and Well-being, law, Endomicroscopy, Computer vision, Artificial intelligence, business
Abstract

Computer-aided detection (CAD) approaches have shown promising results for early esophageal cancer detection using Volumetric Laser Endoscopy (VLE) imagery. However, the relatively slow and computationally costly tissue segmentation employed in these approaches hamper their clinical applicability. In this paper, we propose to reframe the 2D tissue segmentation problem into a 1D tissue boundary detection problem. Instead of using an encoder-decoder architecture, we propose to follow the tissue boundary using a Recurrent Neural Network (RNN), exploiting the spatio-temporal relations within VLE frames. We demonstrate a near state-of-the-art performance using 18 times less floating point operations, enabling real-time execution in clinical practice.

Published
2021

29. A computer-assisted algorithm for narrow-band-imaging–based tissue characterization in Barrett’s esophagus

Author

Struyvenberg, Maarten R., De Groof, Albert J., van der Putten, Joost, van der Sommen, Fons, Baldaque-silva, Francisco, Omae, Masami, Pouw, Roos, Bisschops, Raf, Vieth, Michael, Schoon, Erik J., Curvers, Wouter L., de With, Peter H., Bergman, Jacques J., Struyvenberg, Maarten R., De Groof, Albert J., van der Putten, Joost, van der Sommen, Fons, Baldaque-silva, Francisco, Omae, Masami, Pouw, Roos, Bisschops, Raf, Vieth, Michael, Schoon, Erik J., Curvers, Wouter L., de With, Peter H., and Bergman, Jacques J.

Abstract

Background and Aims The endoscopic evaluation of narrow-band imaging (NBI)-zoom imagery in Barrett’s esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett’s mucosa. Our aim was to demonstrate feasibility of a deep-learning CAD system for tissue characterization of NBI-zoom imagery in BE. Methods The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic (ND)BE white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI-zoom images with histological correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed, dataset of 59 neoplastic and 98 NDBE NBI-zoom videos. Performance was evaluated using fourfold cross-validation. Primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI-zoom videos. Results The CAD system demonstrated an accuracy, sensitivity and specificity for detection of BE neoplasia using NBI-zoom images of 84%, 88%, and 78%, respectively. In total 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity and specificity of the video-based CAD system were 83% (95% CI, 78%-89%), 85% (95% CI, 76%-94%) and 83% (95% CI, 76%-90%), respectively. Mean assessment speed was 38 frames per second. Conclusion We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett’s neoplasia on histologically confirmed unaltered NBI-zoom videos with fast corresponding assessment time.

Published
2021

30. First steps into endoscopic video analysis for Barrett's cancer detection

Author

van der Putten, Joost, de Groof, Jeroen, van der Sommen, Fons, Struyvenberg, Maarten, Zinger, Svitlana, Curvers, Wouter, Schoon, Erik, Bergman, Jacques, de With, Peter H.N., Hahn, Horst K., Mazurowski, Maciej A., Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Signal Processing Systems, Biomedical Diagnostics Lab, EAISI Health, Gastroenterology and Hepatology, Graduate School, AGEM - Re-generation and cancer of the digestive system, CCA - Imaging and biomarkers, and AGEM - Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
barrett's esophagus, multi-frame classication, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Esophageal adenocarcinoma, real-time, Cancer detection, Video quality, medicine.disease, Machine learning, computer.software_genre, SDG 3 – Goede gezondheid en welzijn, SDG 3 - Good Health and Well-being, Barrett's esophagus, temporal localization, medicine, In patient, Artificial intelligence, business, Temporal information, computer, Computational budget
Abstract

Routine surveillance endoscopies are currently used to detect dysplasia in patient with Barrett's Esophagus (BE). However, most of these procedures are performed by non-expert endoscopists in community hospitals. Leading to many missed dysplastic lesions, which can progress into advanced esophageal adenocarcinoma if left untreated.1 In recent years, several successful algorithms have been proposed for the detection of cancer in BE using high-quality overview images. This work addresses the first steps towards clinical application on endoscopic surveillance videos. Several challenges are identified that occur when moving from image-based to video-based analysis. (1) It is shown that algorithms trained on high-quality overview images do not naively transfer to endoscopic videos due to e.g. non-informative frames. (2) Video quality is shown to be an important factor in algorithm performance. Specifically, temporal location performance is highly correlated with video quality. (3) When moving to real-time algorithms, the additional compute necessary to address the challenges in videos will become a burden on the computational budget. However, in addition to challenges, videos also bring new opportunities not available in the current image-based methods such as the inclusion of temporal information. This work shows that a multi-frame approach increases performance compared to a naive single-image method when the above challenges are addressed.

Published
2020

31. Influence of decoder size for binary segmentation tasks in medical imaging

Author

van der Putten, Joost, van der Sommen, Fons, de With, Peter H.N., Isgum, Ivana, Landman, Bennett A., Video Coding & Architectures, Center for Care & Cure Technology Eindhoven, Eindhoven MedTech Innovation Center, and EAISI Health

Subjects
Computer science, business.industry, Deep learning, Skin lesions, Inference, Data_CODINGANDINFORMATIONTHEORY, Residual, Encoder-decoder networks, Image (mathematics), Convolution, Data set, Binary segmentation, Model optimization, Medical imaging, Segmentation, Artificial intelligence, business, Algorithm
Abstract

Symmetric design of encoder-decoder networks is common in deep learning. For almost all segmentation problems, the output segmentation is vastly less complex compared to the input image. However, the effect of the size of the decoder on segmentation performance has not been investigated in literature. This work investigates the effect of reducing decoder size on binary segmentation performance in a medical imaging application. To this end, we propose a methodology to reduce the size of the decoder in encoder-decoder networks, where residual skip connections are employed in combination with a 1×1 convolution instead of concatenations (as employed by U-Net) to achieve models with asymmetric design. The results on the ISIC2017 data set show that the amount of trainable parameters in the decoder can be reduced by up to a factor 100 compared to standard U-Net, while retaining segmentation performance. Additionally, the reduced amount of trainable decoder parameters in the proposed models leads to inference times up to 3 times faster compared to standard U-Net.

Published
2020

32. The field effect in Barrett's Esophagus: A macroscopic view using white light endoscopy and deep learning

Author

Verhage, Levi, van der Putten, Joost, van der Sommen, Fons, De Groof, Jeroen, Struyvenberg, Maarten, de With, Peter H.N., Hahn, Horst K., Mazurowski, Maciej A., Electrical Engineering, Center for Care & Cure Technology Eindhoven, Video Coding & Architectures, Eindhoven MedTech Innovation Center, EAISI Health, Gastroenterology and Hepatology, Graduate School, AGEM - Re-generation and cancer of the digestive system, CCA - Imaging and biomarkers, and AGEM - Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
medicine.medical_specialty, barrett's esophagus, field effect, business.industry, Deep learning, Significant difference, deep learning, medicine.disease, Convolutional neural network, medicine.anatomical_structure, Dysplasia, Barrett's esophagus, White light endoscopy, convolutional neural networks, medicine, Radiology, Artificial intelligence, Stage (cooking), Esophagus, business, field eect
Abstract

Over the past few decades, primarily developed countries witnessed an increased incidence of esophageal adenocarcinoma (EAC). Screening and surveillance of Barrett's esophagus (BE), which is known to augment the probability of developing EAC, can significantly improve survival rates. This is because early-stage dysplasia in BE can be treated effectively, while each subsequent stage complicates successful treatment and seriously reduces survival rates. This study proposes a convolutional neural network-based algorithm, which classifies images of BE visualized with White Light Endoscopy (WLE) as either dysplastic or non-dysplastic. To this end, we use merely pixels surrounding the dysplastic region, while excluding the pixels covering the dysplastic region itself. The phenomenon where the diagnosis of a patient can be determined from tissue other than the clearly observable diseased area, is termed the field effect. With its potential to identify missed lesions, it may prove to be a helpful innovation in the screening and surveillance process of BE. A statistical significant difference test indicates the presence of the field effect in WLE, when comparing the distribution of the algorithm classifications of unseen data and the distribution obtained by a random classification.

Published
2020

33. VIDEO-BASED COMPUTER AIDED DETECTION SYSTEM DETECTS BARRETT’S NEOPLASIA WITH HIGH ACCURACY DURING LIVE ENDOSCOPIC PROCEDURES: A MULTI-CENTER PILOT AND FEASIBILITY STUDY

Author

Fockens, Kiki, Jukema, Jelmer, Jong, Martijn, Boers, Tim, Kusters, Koen, Van Der Putten, Joost, Pouw, Roos, Duits, Lucas, Weusten, Bas, Herrero, L. Alvarez, Houben, Martin, Nagengast, Wouter, Westerhof, Jessie, Alkhalaf, A., Mallant-Hent, Rosalie, Scholten, Pieter, Ragunath, Krish, Seewald, Stefan, Elbe, Peter, Baldaque-Silva, Francisco, Barret, Maximilien, Fernández-Sordo, Jacobo Ortiz, Villarejo, Guiomar Moral, Pech, Oliver, Beyna, Torsten, Van Der Sommen, Fons, De With, P.H.N., De Groof, Jeroen, and Bergman, Jacques

Published
2023
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35. A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett’s esophagus

Author

Struyvenberg, Maarten R., primary, de Groof, Albert J., additional, van der Putten, Joost, additional, van der Sommen, Fons, additional, Baldaque-Silva, Francisco, additional, Omae, Masami, additional, Pouw, Roos, additional, Bisschops, Raf, additional, Vieth, Michael, additional, Schoon, Erik J., additional, Curvers, Wouter L., additional, de With, Peter H., additional, and Bergman, Jacques J., additional

Published
2021
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36. 1016 DOMAIN-SPECIFIC PRETRAINING OF DEEP LEARNING SYSTEMS IN GASTROINTESTINAL ENDOSCOPY IMPROVES PERFORMANCE OVER CURRENT STATE-OF-THE-ART PRETRAINING METHODS

Author

Fockens, Kiki, Boers, Tim G., Jukema, Jelmer, Jong, Martijn R., Kusters, Koen C., Van Der Putten, Joost, Struyvenberg, Maarten, Pouw, Roos E., Duits, Lucas C., Weusten, Bas L., Herrero, L. Alvarez, Houben, Martin H., Nagengast, Wouter B., Westerhof, Jessie, Alkhalaf, A., Mallant-Hent, Rosalie, Scholten, Pieter, Ragunath, Krish, Seewald, Stefan, Elbe, Peter, Baldaque-Silva, Francisco, Barret, Maximilien, Fernández-Sordo, Jacobo Ortiz, Villarejo, Guiomar Moral, Pech, Oliver, Beyna, Torsten, Van Der Sommen, Fons, De With, P.H.N., De Groof, Jeroen, and Bergman, Jacques

Published
2023
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39. 1073 COMPUTER ALGORITHM DETECTS EARLY BARRETT'S CANCER WITH HIGH ACCURACY DURING LIVE ENDOSCOPIC PROCEDURES

Author

Struyvenberg, Maarten R., primary, de Groof, Jeroen, additional, Fockens, Kiki, additional, van der Putten, Joost, additional, van der Sommen, Fons, additional, Boers, Tim G., additional, Bisschops, Raf, additional, de With, P.H.N., additional, Pouw, Roos E., additional, Curvers, Wouter, additional, Schoon, Erik.J., additional, and Bergman, Jacques, additional

Published
2020
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40. 244 COMPUTER-AIDED DETECTION ALGORITHM DETECTS BARRETT NEOPLASIA WITH HIGH DIAGNOSTIC ACCURACY DURING LIVE ENDOSCOPIC PROCEDURES: A PILOT STUDY.

Author

Struyvenberg, Maarten R., primary, de Groof, Jeroen, additional, Fockens, Kiki, additional, van der Putten, Joost, additional, van der Sommen, Fons, additional, Boers, Tim G., additional, Zinger, S., additional, Bisschops, Raf, additional, de With, P.H.N., additional, Pouw, Roos E., additional, Curvers, Wouter, additional, Schoon, Erik, additional, and Bergman, Jacques, additional

Published
2020
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42. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Author

van der Sommen, Fons, primary, de Groof, Jeroen, additional, Struyvenberg, Maarten, additional, van der Putten, Joost, additional, Boers, Tim, additional, Fockens, Kiki, additional, Schoon, Erik J, additional, Curvers, Wouter, additional, de With, Peter, additional, Mori, Yuichi, additional, Byrne, Michael, additional, and Bergman, Jacques J G H M, additional

Published
2020
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43. Mo1163 ENDOSCOPY-DRIVEN ALGORITHM IDENTIFIES BARRETT NEOPLASIA WITH HIGH ACCURACY USING NBI-ZOOM VIDEOS: AN INTERNATIONAL MULTICENTER STUDY.

Author

Struyvenberg, Maarten R., primary, de Groof, Jeroen, additional, van der Putten, Joost, additional, van der Sommen, Fons, additional, Baldaque-Silva, Francisco, additional, Omae, Masami, additional, Bisschops, Raf, additional, Schoon, Erik, additional, Curvers, Wouter, additional, de With, P.H.N., additional, and Bergman, Jacques, additional

Published
2020
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48. Deep-Learning System Detects Neoplasia in Patients With Barrett’s Esophagus With Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study With Benchmarking

Author

de Groof, Albert J., primary, Struyvenberg, Maarten R., additional, van der Putten, Joost, additional, van der Sommen, Fons, additional, Fockens, Kiki N., additional, Curvers, Wouter L., additional, Zinger, Sveta, additional, Pouw, Roos E., additional, Coron, Emmanuel, additional, Baldaque-Silva, Francisco, additional, Pech, Oliver, additional, Weusten, Bas, additional, Meining, Alexander, additional, Neuhaus, Horst, additional, Bisschops, Raf, additional, Dent, John, additional, Schoon, Erik J., additional, de With, Peter H., additional, and Bergman, Jacques J., additional

Published
2020
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50. Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function

Author

van der Putten, Joost, van der Sommen, Fons, Struyvenberg, Maarten, de Groof, Jeroen, Curvers, Wouter, Schoon, Erik, J.G.H.M. Bergman, Jacques, de With, Peter H.N., Angelini, Elsa D., Landman, Bennett A., Gastroenterology and Hepatology, AGEM - Re-generation and cancer of the digestive system, CCA - Imaging and biomarkers, Video Coding & Architectures, and Center for Care & Cure Technology Eindhoven

Subjects
Ground truth, VLE, Pixel, business.industry, Computer science, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, SDG 3 – Goede gezondheid en welzijn, 021001 nanoscience & nanotechnology, Barrett's Esophagus, Cancer detection, 01 natural sciences, 010309 optics, SDG 3 - Good Health and Well-being, 0103 physical sciences, Endomicroscopy, Segmentation, CAD, Artificial intelligence, 0210 nano-technology, business
Abstract

Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. Especially Computer Aided Detection (CAD) techniques show great promise compared to medical doctors, who cannot reliably find disease patterns in the noisy VLE signal. However, an essential pre-processing step for the CAD system is tissue segmentation. At present, tissue is segmented manually but is not scalable for the entire VLE scan consisting of 1,200 frames of 4,096 × 2,048 pixels. Furthermore, the current CAD methods cannot use the VLE scans to their full potential, as only a small segment of the esophagus is selected for further processing, while an automated segmentation system results in significantly more available data. This paper explores the possibility of automatically segmenting relevant tissue for VLE scans using FusionNet and a domain-specific loss function. The contribution of this work is threefold. First, we propose a tissue segmentation algorithm for VLE scans. Second, we introduce a weighted ground truth that exploits the signal-to-noise ratio characteristics of the VLE data. Third, we compare our algorithm segmentation against two additional VLE experts. The results show that our algorithm annotations are indistinguishable from the expert annotations and therefore the algorithm can be used as a preprocessing step for further classification of the tissue.

Published
2019

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