Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application

Pranita Pradhan, Tobias Meyer, Michael Vieth, Andreas Stallmach, Maximilian Waldner, Michael Schmitt, Juergen Popp, Thomas Bocklitz

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 model 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.

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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