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 |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Van-Linh surname: Le fullname: Le, Van-Linh email: van-linh.le@u-bordeaux.fr organization: LaBRI - University of Bordeaux, UMR 5800, 351, cours de la Liberation, 33405 Talence, France – sequence: 2 givenname: Marie surname: Beurton-Aimar fullname: Beurton-Aimar, Marie email: beurton@labri.fr organization: LaBRI - University of Bordeaux, UMR 5800, 351, cours de la Liberation, 33405 Talence, France – sequence: 3 givenname: Akka surname: Zemmari fullname: Zemmari, Akka email: zemmari@labri.fr organization: LaBRI - University of Bordeaux, UMR 5800, 351, cours de la Liberation, 33405 Talence, France – sequence: 4 givenname: Alexia surname: Marie fullname: Marie, Alexia email: alexia.marie@inrae.fr organization: UMR 1349 IGEPP, BP 35327, 35653 Le Rheu, France – sequence: 5 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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CitedBy_id | crossref_primary_10_1016_j_compag_2021_106058 crossref_primary_10_3389_fevo_2021_642774 crossref_primary_10_1002_ajpa_24531 crossref_primary_10_2142_biophysico_bppb_v19_0011 crossref_primary_10_3390_biology12030418 crossref_primary_10_3390_insects14050430 crossref_primary_10_1016_j_ecoinf_2022_101694 crossref_primary_10_1038_s41559_024_02438_0 crossref_primary_10_1051_bioconf_202410823001 crossref_primary_10_1016_j_jpra_2023_07_002 crossref_primary_10_1111_jen_13227 |
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Keywords | Deep learning Landmarks Morphometry Convolutional neural network |
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