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Automated landmarking for insects morphometric analysis using deep neural networks

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorLE, Van-Linh
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBEURTON-AIMAR, Marie
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorZEMMARI, Akka
hal.structure.identifierInstitut de Génétique, Environnement et Protection des Plantes [IGEPP]
dc.contributor.authorMARIE, Alexia
hal.structure.identifierInstitut de Génétique, Environnement et Protection des Plantes [IGEPP]
dc.contributor.authorPARISEY, Nicolas
dc.date.accessioned2024-04-04T02:45:50Z
dc.date.available2024-04-04T02:45:50Z
dc.date.issued2020-11
dc.identifier.issn1574-9541
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191501
dc.description.abstractEnLandmarks are one of the important concepts in morphometry analysis. They are anatomical points that can be located consistently (e.g., corner of the eyes) and used to establish correspondence or divergence among morphologies of biological or non-biological specimens. Currently, the landmarks are mostly positioned manually by entomologists on numerical images. In this work, we propose a method to automatically predict the landmarks on entomological images based on Deep Learning methods, more specifically by using Convolutional Neural Network (CNN). We propose a CNN architecture, EB-Net, which is built in a modular way the concept of "Elementary Blocks", each made up of usual layer types of CNN. After using a custom data augmentation procedure, the network has been trained and tested on a data set of different anatomical part of carabids (pronotum, head and elytra). In this numerical experiment, we have generated two strategies to evaluate the network and to improve the obtained results: training from scratch or applying a fine-tuning step. The predicted landmark coordinates have been compared to the coordinates of the manual landmarks provided by the biologists. The statistical analysis of the distances between predicted and manual coordinates has shown that our predictions can replace efficiently manual landmarking and allows to propose automatization of such operation.
dc.language.isoen
dc.publisherElsevier
dc.subject.enLandmarks
dc.subject.enMorphometry
dc.subject.enDeep learning
dc.subject.enConvolutional neural network
dc.title.enAutomated landmarking for insects morphometric analysis using deep neural networks
dc.typeArticle de revue
dc.identifier.doi10.1016/j.ecoinf.2020.101175
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalEcological Informatics
bordeaux.volume60
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03319822
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03319822v1
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