Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure.
Environ Sci Technol
; 54(16): 10022-10030, 2020 08 18.
Article
em En
| MEDLINE
| ID: mdl-32663392
ABSTRACT
While the use of deep learning is a valuable technology for automatic detection systems for medical data and images, the biofouling community is still lacking an analytical tool for the detection and counting of diatoms on samples after short-term field exposure. In this work, a fully convolutional neural network was implemented as a fast and simple approach to detect diatoms on two-channel (fluorescence and phase-contrast) microscopy images by predicting bounding boxes. The developed approach performs well with only a small number of trainable parameters and a F1 score of 0.82. Counting diatoms was evaluated on a data set of 600 microscopy images of three different surface chemistries (hydrophilic and hydrophobic) and is very similar to counting by humans while demanding only a fraction of the analysis time.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diatomáceas
/
Incrustação Biológica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Environ Sci Technol
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Alemanha