Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application

Conference contribution


Publication Details

Author(s): Pradhan P, Meyer T, Vieth M, Stallmach A, Waldner M, Schmitt M, Popp J, Bocklitz T
Publisher: SciTePress
Publication year: 2019
Conference Proceedings Title: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
Pages range: 396-405
ISBN: 9789897583513


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.


FAU Authors / FAU Editors

Waldner, Maximilian Prof. Dr.
Professur für Funktionelle Bildgebung in der Medizin


Additional Organisation
Erlangen Graduate School in Advanced Optical Technologies


External institutions with authors

Friedrich-Schiller-Universität Jena
Klinikum Bayreuth
Leibniz-Institut für Photonische Technologien e.V.
Universitätsklinikum Jena


How to cite

APA:
Pradhan, P., Meyer, T., Vieth, M., Stallmach, A., Waldner, M., Schmitt, M.,... Bocklitz, T. (2019). Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application. In ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (pp. 396-405). Prague, CZ: SciTePress.

MLA:
Pradhan, Pranita, et al. "Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application." Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019, Prague SciTePress, 2019. 396-405.

BibTeX: 

Last updated on 2019-15-05 at 10:38