Prof. Jacques J.G.H.M. Bergman Profile

Prof. Jacques J.G.H.M. Bergman

at Amsterdam UMC

SPIE Involvement:

Author

Publications (13)

Proceedings Article | 7 April 2023 Poster + Paper

Barrett's lesion detection using a minimal integer-based neural network for embedded systems integration

Tim G. Boers, Carolus H. Kusters, Kiki Fockens, Jelmer Jukema, Martijn Jong, Jeroen de Groof, Jacques Bergman, Fons van der Sommen, Peter H. de With

Proceedings Volume 12465, 1246527 (2023) https://doi.org/10.1117/12.2653890

KEYWORDS: Quantization, Image segmentation, Neural networks, Embedded systems

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Proceedings Article | 7 April 2023 Poster + Paper

Real-time Barrett's neoplasia characterization in NBI videos using an int8-based quantized neural network

Carolus H. Kusters, Tim G. Boers, Jelmer Jukema, Martijn Jong, Kiki Fockens, Albert de Groof, Jacques Bergman, Fons van der Sommen, Peter H. de With

Proceedings Volume 12465, 124651P (2023) https://doi.org/10.1117/12.2647557

KEYWORDS: Computer aided detection, Endoscopy

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Proceedings Article | 4 April 2022 Presentation + Paper

Efficient endoscopic frame informativeness assessment by reusing the encoder of the primary CAD task

Fidan Mammadli, Fons van der Sommen, Tim Boers, Joost van der Putten, Kiki Fockens, Jelmer Jukema, Martijn de Jong, Jacques J.G.H.M. Bergman, Peter H. de With

Proceedings Volume 12033, 120331C (2022) https://doi.org/10.1117/12.2611133

KEYWORDS: Convolutional neural networks, Computer aided diagnosis and therapy, Computer vision technology, Artificial intelligence, Artificial neural networks, Endoscopy, CAD systems, Image quality, Algorithm development, Network architectures, Video, Detection and tracking algorithms

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

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

Sanne Okel, Fons van der Sommen, Endi Selmanaj, Joost van der Putten, Maarten Struyvenberg, Jacques J.G.H.M. Bergman, Peter H. de With

Proceedings Volume 11597, 115972H (2021) https://doi.org/10.1117/12.2579751

KEYWORDS: Image segmentation, Tissues, Neural networks, Endomicroscopy, Laser tissue interaction, Solid modeling, Computer aided diagnosis and therapy, Esophagus, Convolutional neural networks, Computer aided design

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

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

Endi Selmanaj, Fons van der Sommen, Sanne Okel, Joost van der Putten, Maarten Struyvenberg, Jacques J. G. H. M. Bergman, Peter H. de With

Proceedings Volume 11596, 1159635 (2021) https://doi.org/10.1117/12.2579573

KEYWORDS: Tissues, Laser tissue interaction, Endomicroscopy, Convolutional neural networks, Esophagus, Cancer, New and emerging technologies, Network architectures, Laser applications, Computer-aided diagnosis

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Proceedings Article | 1 April 2020 Presentation

Subdiffuse model to measure tissue optical properties with single fiber reflectance spectroscopy applied to esophageal cancer (Conference Presentation)

Anouk Post, Dirk Faber, Jeroen A. de Groof, Xu Zhang, Martijn D. de Bruin, Theo J. Ruers, Dick J. C. Sterenborg, Wouter Curvers, Jacques J.G.H. Bergman, Ton van Leeuwen

Proceedings Volume 11362, 113620C (2020) https://doi.org/10.1117/12.2554171

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The most common type of cancer are carcinomas: cancers which originate from the epithelial tissue lining the outer surfaces of organs. To detect carcinomas at an early stage, techniques are required with small sampling volumes. Single Fiber Reflectance spectroscopy (SFR) is a promising technique to detect early-stage carcinomas since it has a measurement volume in the order of hundreds of microns. SFR uses a single fiber to emit and collect broadband light. The model from Kanick et al. to relate SFR reflectance to tissue optical properties provided accurate results for tissue with a modified Henyey Greenstein phase function only. However, in many tissues other types of phase functions have been measured. We have developed a new model to relate SFR reflectance to the scattering and absorption properties of tissue, which provides accurate results for a large range of tissue phase functions. SFR measurements fall into the sub-diffuse regime. We, therefore, describe the SFR reflectance as a diffuse plus a semi-ballistic component. An accurate description of the diffuse SFR signal requires double integration of spatially resolved reflectance over the fiber surface. We use approaches from Geometric Probability and have derived the first analytic solution for the diffuse contribution to SFR. For the semi-ballistic contribution to the SFR signal we introduce a new phase function dependent parameter, psb, to describe the semi-ballistic part of the SFR signal. We will use the model to derive optical properties from SFR measurements performed endoscopically in patients with Barrett’s esophagus. These patients are at an increased risk to develop esophageal adenocarcinoma and, therefore, undergo regular endoscopic surveillance. When detected at an early stage, endoscopic treatment is possible, thereby avoiding extensive surgery. Based on the concept of field cancerization, we investigated whether SFR could be used as a tool to identify which patients are developing esophageal adenocarcinoma.

Proceedings Article | 16 March 2020 Paper

First steps into endoscopic video analysis for Barrett’s cancer detection: challenges and opportunities

Joost van der Putten, Jeroen de Groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter H. de With

Proceedings Volume 11314, 1131431 (2020) https://doi.org/10.1117/12.2544229

KEYWORDS: Video, Video surveillance, Endoscopy, Cancer, Esophagus, Computer aided design, Image classification

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Proceedings Article | 10 March 2020 Presentation + Paper

Detection of frame informativeness in endoscopic videos using image quality and recurrent neural networks

T. G. W. Boers, J. van der Putten, J. de Groof, M. Struyvenberg, K. Fockens, W. Curvers, E. Schoon, F. van der Sommen, J. Bergman, P.H. de With

Proceedings Volume 11313, 1131315 (2020) https://doi.org/10.1117/12.2545734

KEYWORDS: Endoscopy, Image quality, Video, Neural networks, Artificial intelligence, Esophagus, Visualization, Image classification, Evolutionary algorithms, Binary data

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Proceedings Article | 15 March 2019 Paper

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

Joost van der Putten, Fons van der Sommen, Maarten Struyvenberg, Jeroen de Groof, Wouter Curvers, Erik Schoon, Jacques J.G.H.M. Bergman, Peter H. de With

Proceedings Volume 10949, 109492J (2019) https://doi.org/10.1117/12.2512192

KEYWORDS: Image segmentation, Optical coherence tomography, Computer aided diagnosis and therapy, Esophagus, Endomicroscopy, Endoscopy

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Proceedings Article | 13 March 2019 Paper

A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Thom Scheeve, Maarten Struyvenberg, Wouter Curvers, Albert de Groof, Erik Schoon, Jacques J. G. H. Bergman, Fons van der Sommen, Peter H. de With

Proceedings Volume 10950, 109501Y (2019) https://doi.org/10.1117/12.2508244

KEYWORDS: Image segmentation, Tissues, Image analysis, In vivo imaging, Endomicroscopy, Esophagus, Endoscopy, Machine learning

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SPIE Journal Paper | 19 December 2017 Open Access

Optical coherence tomography in gastroenterology: a review and future outlook

Tsung-Han Tsai, Cadman Leggett, Arvind Trindade, Amrita Sethi, Anne-Fré Swager, Virendra Joshi, Jacques J. Bergman, Hiroshi Mashimo, Norman Nishioka, Eman Namati

JBO, Vol. 22, Issue 12, 121716, (December 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.12.121716

KEYWORDS: Optical coherence tomography, Endoscopy, Tissues, Gastroenterology, Esophagus, Imaging systems, Diagnostics, Tissue optics, Biopsy, Image resolution

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SPIE Journal Paper | 4 August 2017 Open Access

Quantitative attenuation analysis for identification of early Barrett’s neoplasia in volumetric laser endomicroscopy

Anne-Fré Swager, Dirk Faber, Daniel Martijn de Bruin, Bas Weusten, Sybren Meijer, Jacques Bergman, Wouter Curvers, Ton van Leeuwen

JBO, Vol. 22, Issue 08, 086001, (August 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.8.086001

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Proceedings Article | 3 March 2017 Presentation + Paper

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

Sander Klomp, Fons van der Sommen, Anne-Fré Swager, Svitlana Zinger, Erik Schoon, Wouter Curvers, Jacques Bergman, Peter H. de With

Proceedings Volume 10134, 101340D (2017) https://doi.org/10.1117/12.2253860

KEYWORDS: Esophagus, Cancer, Performance modeling, Visual process modeling, Feature extraction, Image classification, Feature selection, Principal component analysis, Machine learning, Tissues

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Showing 5 of 13 publications

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