Towards the analysis of coral skeletal density-banding using deep learning

Abstract: X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI.

Citation

Rutterford, A., Bertini, L., Hendy, E.J. et al. Towards the analysis of coral skeletal density-banding using deep learning. SN Appl. Sci. 4, 38 (2022). https://doi.org/10.1007/s42452-021-04912-x

Type

Journal Article

Item Description

Copyright © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The linked document is the published version of the article.
NHM Repository

ISSN

2523-3963

EISSN

2523-3971