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

Beitrag bei einer Tagung


Details zur Publikation

Autor(en): Pradhan P, Meyer T, Vieth M, Stallmach A, Waldner M, Schmitt M, Popp J, Bocklitz T
Verlag: SciTePress
Jahr der Veröffentlichung: 2019
Tagungsband: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
Seitenbereich: 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-Autoren / FAU-Herausgeber

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


Zusätzliche Organisationseinheit(en)
Erlangen Graduate School in Advanced Optical Technologies


Autor(en) der externen Einrichtung(en)
Friedrich-Schiller-Universität Jena
Klinikum Bayreuth
Leibniz-Institut für Photonische Technologien e.V.
Universitätsklinikum Jena


Zitierweisen

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: 

Zuletzt aktualisiert 2019-15-05 um 10:38