Automated landmarking for insects morphometric analysis using deep neural networks

• 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; Elsevier
• Subjects: Convolutional neural network; Deep learning; Landmarks; Life Sciences; Morphometry; Deep learning; Landmarks; Morphometry; Convolutional neural network
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2020.101175

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.