Data/hora: 13/06/2024 08:43:57

Provedor de dados: | 5
País: | France
Título: | Optimisation de l’identification et du dénombrement du microphytoplancton avec le système couplé de numérisation et d’analyse d’images FlowCAM – Zoo/PhytoImage (système innovant)
Autores: | Grosjean, Philippe; Wacquet, Guillaume.
Data: | 2016-02
Ano: | 2016
Palavras-chave: | Phytoplancton; REPHY; Analyse d'image; Classification supervisée; Dénombrement de cellules; Apprentissage actif; Manche; Atlantique.; Phytoplankton; REPHY; Image analysis; Machine learning; Cells enumeration; Active learning; The Channel; Atlantic Ocean.
Resumo: | This report details the work accomplished to enhance the Zoo/PhytoImage software to optimize its use for the analysis of phytoplankton samples in general, but more particularly, in the framework of an operational survey of coastal seawater (REPHY, IFREMER). Zoo/PhytoImage allows to analyze “numerically recorded” plankton samples, that is, by using digital images gathered with specialized devices such as the FlowCAM, or the FastCAM (see report 3). A machine learning approach allows to automatically classify the digitized particles into various taxonomic groups. Once this is done, global statistics are calculated on each sample, including the number of particles, the biomass, and the size spectrum per taxonomic group. Two major changes are introduced in the calculations done by Zoo/PhytoImage: - the enumeration of the cells per colonies allows to express results per cell, where previous versions of the software only expressed results per colonies, - an active learning algorithm is implemented in order to optimize the validation step (manual correction of the residual error after automatic classification of the particles by the computer). This way, the software will learn how to perform that correction based on the validations done so far by the taxonomist. Consequently, the computer now assists dynamically the user in the verification and correction procedure. The number of items that the user has to manually check is thus greatly reduced in order to reach a given goal (say, less than 5% of residual error in each taxonomic group). The report is made of three complementary parts: - the first two sections (advance reports on March and June 2015) detail the progression in the cells per colonies enumeration and in the implementation of active learning. The former section explores ideas, whereas the latter one finalizes the best solutions and tests them on actual samples from the REPHY. - the third section is a new French version of the Zoo/PhytoImage user manual that focuses on the analysis of plankton images from the FlowCAM (and the FastCAM), including the new functionalities. Of course, this user manual comes with the code of the new version 5.4.0 of Zoo/PhytoImage that fully implements these new features. A version 6.0 will be made public shorty, at the end of the beta test period: it will be very close to the version 5.4.0 described in the present report. An optimization of the validation procedure was also developped. The validation of samples is now performed with complex statistic tools : (i) the selection of particles which will be checked by the technician is determined in a probabilist way (detection of suspects), (ii) a second classification is used to assist the user in validation (active learning), (iii) finally, a statistical correction algorithm of error is implemented on the results. This reduces the validation work. This means that the taxonomist may stop earlier and manually correct the allocation of taxonomic groups to suspicious particles. The progress of the validation work is clearly presented and the number of steps necessary to achieve a given objective (eg, less than 5% error in all taxonomic groups) is anticipated. All these measures contribute to both comfort and efficiency of validation.
Tipo: | Text
Idioma: | Francês

Identificador:

http://archimer.ifremer.fr/doc/00389/49990/50578.pdf

http://archimer.ifremer.fr/doc/00389/49990/

Formato: | application/pdf

Fonte:

2017 Ifremer, ONEMA, Univesité de Mons, ULCO

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