Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks

Type Article
Date 2022-07
Language English
Author(s) Fuchs Robin1, 2, Thyssen MelilotusORCID2, Creach VéroniqueORCID3, Dugenne MathildeORCID4, Izard LloydORCID5, Latimier Marie6, Louchart ArnaudORCID7, 8, Marrec PierreORCID9, Rijkeboer MachteldORCID10, Grégori GéraldORCID2, Pommeret DenysORCID1, 11, 12, 13
Affiliation(s) 1 : Aix Marseille Univ, CNRS, I2M Marseille ,France
2 : Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO Marseille ,France
3 : Cefas, Suffolk, UK
4 : Department of Oceanography University of Hawai'i at Manoa Honolulu Hawai'i ,USA
5 : Sorbonne Université, CNRS, IRD, MNHN, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN‐IPSL) Paris ,France
6 : IFREMER, DYNECO PELAGOS Plouzane, France
7 : Department of Integrative Marine Ecology Stazione Zoologica Anton Dohrn, Villa Comunale Naples, Italy
8 : IFREMER, Laboratoire Environnement and Ressources Boulogne‐sur‐Mer ,France
9 : Graduate School of Oceanography University of Rhode Island Narragansett Rhode Island, USA
10 : Laboratory for Hydrobiological Analysis, Rijkswaterstaat (RWS) Lelystad ,The Netherlands
11 : Université Claude Bernard Lyon 1 Villeurbanne ,France
12 : ISFA Lyon ,France
13 : Laboratoire de Sciences Actuarielle et Financière (SAF) Lyon, France
Source Limnology And Oceanography-methods (1541-5856) (Wiley), 2022-07 , Vol. 20 , N. 7 , P. 387-399
DOI 10.1002/lom3.10493

The variability of phytoplankton distribution has been unraveled by high-frequency measurements. Such a resolution can be approached by automated pulse-shape recording flow cytometry (AFCM) operating at hourly sampling resolution. AFCM records morphological and physiological traits as single-cell optical pulse shapes that can be used to classify cells into phytoplankton functional groups (PFGs). However, the associated manual post-processing of the data coupled with the increasing size and number of datasets is time-consuming and error-prone. Machine learning models are increasingly used to run automatic classification. Yet, most of the existing methods either present a long training process, need to manually design features from the raw optical pulse shapes, or are dedicated to images only. In this study, we present a convolutional neural network (CNN) to classify several PFGs using AFCM pulse shapes. The uncertainties of manual classification were first estimated by comparing experts' recognition of six PFGs. Consensual particles from the manual PFG classification were used to train and validate the CNN. The CNN obtained competitive performances compared to other models used in the literature and remained robust across several sampling areas, and instrumental hardware and settings. Finally, we assessed the ability of this classifier to predict phytoplankton counts at a Mediterranean coastal station and from a cruise in the South-West Indian Ocean, providing a comparison with the manual classification over 3-month periods and a 2h frequency. These promising results strengthen the near real-time observation of PFGs, especially required with the increasing use of AFCM in monitoring research programs.

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Fuchs Robin, Thyssen Melilotus, Creach Véronique, Dugenne Mathilde, Izard Lloyd, Latimier Marie, Louchart Arnaud, Marrec Pierre, Rijkeboer Machteld, Grégori Gérald, Pommeret Denys (2022). Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks. Limnology And Oceanography-methods, 20(7), 387-399. Publisher's official version : , Open Access version :