Deep learning-based photodamage reduction on harmonic generation microscope at low-level optical power.
J Biophotonics
; 17(1): e202300285, 2024 01.
Article
de En
| MEDLINE
| ID: mdl-37738103
ABSTRACT
The trade-off between high-quality images and cellular health in optical bioimaging is a crucial problem. We demonstrated a deep-learning-based power-enhancement (PE) model in a harmonic generation microscope (HGM), including second harmonic generation (SHG) and third harmonic generation (THG). Our model can predict high-power HGM images from low-power images, greatly reducing the risk of phototoxicity and photodamage. Furthermore, the PE model trained only on normal skin data can also be used to predict abnormal skin data, enabling the dermatopathologist to successfully identify and label cancer cells. The PE model shows potential for in-vivo and ex-vivo HGM imaging.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Apprentissage profond
Type d'étude:
Prognostic_studies
Langue:
En
Journal:
J Biophotonics
Sujet du journal:
BIOFISICA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Taïwan