at Google ScholarTitle data
Pradhan, Pranita ; Meyer, Tobias ; Vieth, Michael ; Stallmach, Andreas ; Waldner, Maximilian ; Schmitt, Michael ; Popp, Jürgen ; Bocklitz, Thomas:
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. -
Setúbal
: SciTePress
,
2019
. - pp. 396-405
ISBN 978-989-758-351-3
DOI: https://doi.org/10.5220/0007314003960405
Abstract in another language
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.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | Yes |
| Keywords: | Semantic Segmentation; Non-linear Multimodal Imaging; Inflammatory Bowel Disease |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 15 May 2023 12:43 |
| Last Modified: | 22 May 2023 11:49 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76356 |