bei Google ScholarTitelangaben
Pradhan, Pranita ; Meyer, Tobias ; Vieth, Michael ; Stallmach, Andreas ; Waldner, Maximilian ; Schmitt, Michael ; Popp, Jürgen ; Bocklitz, Thomas:
Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning.
In: Biomedical Optics Express.
Bd. 12
(2021)
Heft 4
.
- S. 2280-2298.
ISSN 2156-7085
DOI: https://doi.org/10.1364/BOE.415962
Abstract
Hematoxylin and Eosin (H&E) staining is the ’gold-standard’ method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for ’real-time’ disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author’s best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| 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 |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 11 Mai 2023 12:15 |
| Letzte Änderung: | 11 Mai 2023 12:15 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76400 |