at Google ScholarTitle data
Rodner, Erik ; Bocklitz, Thomas ; von Eggeling, Ferdinand ; Ernst, Günther ; Chernavskaia, Olga ; Popp, Jürgen ; Denzler, Joachim ; Guntinas-Lichius, Orlando:
Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma : pilot study.
In: Head & Neck.
Vol. 41
(2019)
Issue 1
.
- pp. 116-121.
ISSN 1097-0347
DOI: https://doi.org/10.1002/hed.25489
Abstract in another language
Background
A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed.
Methods
Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types.
Results
A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset.
Conclusion
Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| 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 |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 15 May 2023 12:08 |
| Last Modified: | 15 May 2023 12:08 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76360 |