Data/hora: 13/06/2024 02:30:41

Provedor de dados: | 5
País: | France
Título: | Version évolutive de l’outil opérationnel de numérisation et d’analyse semi-automatique d’images de phytoplancton, utilisant le matériel FlowCAM et le logiciel ZooPhytoImage. Nouvelles perspectives
Autores: | Wacquet, Guillaume; Grosjean, Philippe; Colas, Florent; Hamad, Denis; Artigas, Luis Felipe.
Data: | 2015-02
Ano: | 2015
Palavras-chave: | Plancton; Analyse automatisée; Analyse d'image; Classification supervisée; Apprentissage actif; Dénombrement de cellules; Plankton; Automated analysis; Image processing; Supervised classification; Active learning; Cells counting.
Resumo: | The coupled system FlowCAM/ZooPhytoImage has become a real operational tool in 2014. However, to be fully adapted to the observations of phytoplankton performed in the context of the REPHY observation network and in order to better respond to present and future requests concerning the evaluation of quality of coastal and marine waters within the European requirements, such as the WFD and MSFD, new functionalities must be integrated into existing tools. Therefore, different axis of development have been proposed by UMONS and Ifremer to adapt both the digitization device and the Zoo/PhytoImage software to the constraints defined by the REPHY. First, version 5 of Zoo/PhytoImage contains recent innovations such as the development of routines to automatically import and analyze data, and the redesign of the graphical user interface to improve the ergonomics and the ease of use. The latter has been studied from the point of view of its fit for specific treatments related to the needs of major partners. Indeed, it appears desirable to significantly increase the visual interaction with the software, particularly in terms of exploration of vignettes and raw data, and the automation of some tasks. For this, in this version of the software, the image processing tools and raw data transformation are implicitly applied. Second, the latest version of Zoo/PhytoImage provides relevant automatic identifications of phytoplankton but without distinction of a cell from a colony. However, even if the colonies greatly contribute to the annual productivity, all the estimators of biomass are essentially calibrated on the abundance in terms of cells per volume unit. In this report, the proposed method consists in building predictive models to estimate the number of cells per colony, based on manual counts performed on the particles of the training set. In this study, high performance scores obtained by different predictive methods, were highlighted on six taxonomic groups of colonial phytoplankton from eastern Channel and Southern North Sea. Finally, the error correction module, integrated to Zoo/PhytoImage since version 4, provides identifications with a low percentage of error per group for each analyzed sample. We propose here to use the information related to the manual validation of vignettes by expert to open the way to the active learning. In this report, we show the interest of this process for the semi-automated recognition of phytoplankton, such as the building and automatic adaptation of the training set allowing to use a "global" training set at national level; the improvement of clustering performances for new samples; a time saving during the validation of the automatic predictions in the context of the error correction.
Tipo: | Text
Idioma: | Francês

Identificador:

http://archimer.ifremer.fr/doc/00389/49986/50573.pdf

http://archimer.ifremer.fr/doc/00389/49986/

Formato: | application/pdf

Fonte:

2015 Ifremer, Onema, Univesité de Mons, ULCO

info:eu-repo/semantics/openAccess

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