Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Titel (primär) Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton
Autor Dunker, S.; Boho, D.; Wäldchen, J.; Mäder, P.;
Journal / Serie BMC Ecology
Erscheinungsjahr 2018
Department iDiv; PHYDIV;
Band/Volume 18
Sprache englisch;
POF III (gesamt) T11;
Keywords Imaging flow cytometry; Phytoplankton; Morphology; Deep learning; CNN; Images; Image-based identification; Machine learning; High throughput cytometry; Magnification
Abstract

Background

Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy with high throughput and latest development in machine learning techniques can overcome this hurdle.

Results

In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage during the batch cultivation. The results show the high potential of this approach, where species identity and their respective life cycle stage could be predicted with a high accuracy of 97%.

Conclusions

These findings could pave the way for reliable and fast phytoplankton species determination of indicator species as a crucial step in water quality assessment.

ID 21235
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=21235
Dunker, S., Boho, D., Wäldchen, J., Mäder, P. (2018):
Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton
BMC Ecology 18 , art. 51