bei Google ScholarTitelangaben
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
. - S. 396-405
ISBN 978-989-758-351-3
DOI: https://doi.org/10.5220/0007314003960405
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.
Weitere Angaben
| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | Semantic Segmentation; Non-linear Multimodal Imaging; Inflammatory Bowel Disease |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz Fakultäten Fakultäten > Fakultät für Mathematik, Physik und Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 15 Mai 2023 12:43 |
| Letzte Änderung: | 22 Mai 2023 11:49 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76356 |