Automated landmarking for insects morphometric analysis using deep neural networks

Landmarks 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 mo...

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Published in:Ecological informatics Vol. 60; p. 101175
Main Authors: Le, Van-Linh, Beurton-Aimar, Marie, Zemmari, Akka, Marie, Alexia, Parisey, Nicolas
Format: Journal Article
Language:English
Published: Elsevier B.V 01-11-2020
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Abstract Landmarks 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. •Providing a new optional for landmarking on biological images.•Proposing another strategy for data augmentation in deep learning•Applying different scenarios to apply deep learning for landmarking.
AbstractList Landmarks 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. •Providing a new optional for landmarking on biological images.•Proposing another strategy for data augmentation in deep learning•Applying different scenarios to apply deep learning for landmarking.
Landmarks 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.
ArticleNumber 101175
Author Marie, Alexia
Zemmari, Akka
Le, Van-Linh
Beurton-Aimar, Marie
Parisey, Nicolas
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  givenname: Alexia
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  givenname: Nicolas
  surname: Parisey
  fullname: Parisey, Nicolas
  email: nicolas.parisey@inrae.fr
  organization: UMR 1349 IGEPP, BP 35327, 35653 Le Rheu, France
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Keywords Deep learning
Landmarks
Morphometry
Convolutional neural network
Language English
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Snippet Landmarks 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...
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elsevier
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SubjectTerms Convolutional neural network
Deep learning
Landmarks
Life Sciences
Morphometry
Title Automated landmarking for insects morphometric analysis using deep neural networks
URI https://dx.doi.org/10.1016/j.ecoinf.2020.101175
https://hal.inrae.fr/hal-03319822
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