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Authors: Pranita Pradhan 1 ; Tobias Meyer 2 ; Michael Vieth 3 ; Andreas Stallmach 4 ; Maximilian Waldner 5 ; Michael Schmitt 6 ; Juergen Popp 1 and Thomas Bocklitz 1

Affiliations: 1 Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies Jena, Germany ; 2 Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies Jena, Germany ; 3 Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany ; 4 Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena, Germany ; 5 Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University of Erlangen-Nuremberg, Germany, Medical Department 1, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany ; 6 Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany

ISBN: 978-989-758-351-3

Keyword(s): Semantic Segmentation, Non-linear Multimodal Imaging, Inflammatory Bowel Disease.

Abstract: Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mode l achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study. (More)

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Paper citation in several formats:
Pradhan, P.; Meyer, T.; Vieth, M.; Stallmach, A.; Waldner, M.; Schmitt, M.; Popp, J. and Bocklitz, T. (2019). Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 396-405. DOI: 10.5220/0007314003960405

@conference{icpram19,
author={Pranita Pradhan. and Tobias Meyer. and Michael Vieth. and Andreas Stallmach. and Maximilian Waldner. and Michael Schmitt. and Juergen Popp. and Thomas Bocklitz.},
title={Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={396-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007314003960405},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application
SN - 978-989-758-351-3
AU - Pradhan, P.
AU - Meyer, T.
AU - Vieth, M.
AU - Stallmach, A.
AU - Waldner, M.
AU - Schmitt, M.
AU - Popp, J.
AU - Bocklitz, T.
PY - 2019
SP - 396
EP - 405
DO - 10.5220/0007314003960405

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