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|Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms|Zetsche, E.-M.; El Mallahi, A.; Dubois, F.; Yourassowsky, C.; Kromkamp, J.C.; Meysman, F.J.R. (2014). Imaging-in-flow: digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms. Limnol. Oceanogr., Methods 42(12): 757-775. dx.doi.org/10.4319/lom.2014.12.757
In: Limnology and Oceanography: Methods. American Society of Limnology and Oceanography: Waco, Tex.. ISSN 1541-5856; e-ISSN 1541-5856, meer
|Auteurs|| || Top |
- Zetsche, E.-M., meer
- El Mallahi, A.
- Dubois, F.
- Yourassowsky, C.
- Kromkamp, J.C., meer
- Meysman, F.J.R., meer
Traditional taxonomic identification of planktonic organisms is based on light microscopy, which is both time-consuming and tedious. In response, novel ways of automated (machine) identification, such as flow cytometry, have been investigated over the last two decades. To improve the taxonomic resolution of particle analysis, recent developments have focused on "imaging-in-flow," i.e., the ability to acquire microscopic images of planktonic cells in a flow-through mode. Imaging-in-flow systems are traditionally based on classical brightfield microscopy and are faced with a number of issues that decrease the classification performance and accuracy (e. g., projection variance of cells, migration of cells out of the focus plane). Here, we demonstrate that a combination of digital holographic microscopy (DHM) with imaging-in-flow can improve the detection and classification of planktonic organisms. In addition to light intensity information, DHM provides quantitative phase information, which generates an additional and independent set of features that can be used in classification algorithms. Moreover, the capability of digitally refocusing greatly increases the depth of field, enables a more accurate focusing of cells, and reduces the effects of position variance. Nanoplanktonic organisms similar in shape were successfully classified from images captured with an off-axis DHM with partial coherence. Textural features based on DHM phase information proved more efficient in separating the three tested phytoplankton species compared with shape-based features or textural features based on light intensity. An overall classification score of 92.4% demonstrates the potential of holographic-based imaging-in-flow for similar looking organisms in the nanoplankton range.