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 and Germany
;
2
Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies Jena and Germany
;
3
Institute of Pathology, Klinikum Bayreuth, Bayreuth and Germany
;
4
Department of Internal Medicine IV (Gastroenterology, Hepatology, and Infectious Diseases), Jena University Hospital, Jena and 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 and Germany
;
6
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena and Germany
Keyword(s):
Semantic Segmentation, Non-linear Multimodal Imaging, Inflammatory Bowel Disease.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
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 mod
el 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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