Dr. Jeroen A. J. de Groof Profile

Dr. Jeroen A. J. de Groof

at Amsterdam UMC

SPIE Involvement:

Author

Publications (8)

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 | 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

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

Levi Verhage, Joost van der Putten, Fons van der Sommen, Jeroen de Groof, Maarten Struyvenberg, Peter H. de With

Proceedings Volume 11314, 1131437 (2020) https://doi.org/10.1117/12.2549391

KEYWORDS: Data modeling, Esophagus, Algorithm development, Tissues, Performance modeling, Endoscopy, Evolutionary algorithms, Computer aided diagnosis and therapy, Image segmentation, Image classification

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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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