利用卷积神经网络检测沉积物显微切片上硅质微化石的新方法

IF 3.7 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Journal of Geophysical Research: Biogeosciences Pub Date : 2024-09-01 DOI:10.1029/2024JG008047
Camille Godbillot, Ross Marchant, Luc Beaufort, Karine Leblanc, Yves Gally, Thang D. Q. Le, Cristele Chevalier, Thibault de Garidel-Thoron
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引用次数: 0

摘要

保存在沉积物样本中的硅藻群落是了解浮游植物群落过去和现在的动态及其对环境变化反应的宝贵指标。这些研究传统上是通过使用光学显微镜进行计数的方法来实现的,这是一个需要分类学专业知识的耗时过程。随着自动图像采集工作流程的出现,现在可以采集大型图像数据集,但需要高效的预处理方法。在显微镜图像上检测硅藻茎突是一项挑战,因为硅藻茎突的浮雕很低、形状各异,而且容易聚集在一起,无法使用传统的阈值技术。深度学习算法有可能解决这些难题,尤其是在物体检测任务中。在此,我们探索使用基于快速区域的卷积神经网络模型来检测地中海沉积物捕集器系列显微图像中的硅质生物矿物,包括硅藻。我们的工作流程取得了可喜的成果,在应用于地中海硅藻图像测试集时,精确度达到 0.72,召回率达到 0.74。当用于检测这些微化石的碎片时,我们的模型性能会下降;当颗粒聚集或图像失焦时,性能也会下降。当该模型用于不同大洋盆地沉积物的显微镜图像集时,微化石的检测率仍然很高,这表明该模型具有广泛应用于当代和古环境研究的潜力。这种自动化方法为分析复杂样本提供了宝贵的工具,特别是对于在训练数据集中代表性不足的稀有物种。
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A New Method for the Detection of Siliceous Microfossils on Sediment Microscope Slides Using Convolutional Neural Networks

Diatom communities preserved in sediment samples are valuable indicators for understanding the past and present dynamics of phytoplankton communities, and their response to environmental changes. These studies are traditionally achieved by counting methods using optical microscopy, a time-consuming process that requires taxonomic expertise. With the advent of automated image acquisition workflows, large image data sets can now be acquired, but require efficient preprocessing methods. Detecting diatom frustules on microscope images is a challenge due to their low relief, diverse shapes, and tendency to aggregate, which prevent the use of traditional thresholding techniques. Deep learning algorithms have the potential to resolve these challenges, more particularly for the task of object detection. Here we explore the use of a Faster Region-based Convolutional Neural Network model to detect siliceous biominerals, including diatoms, in microscope images of a sediment trap series from the Mediterranean Sea. Our workflow demonstrates promising results, achieving a precision score of 0.72 and a recall score of 0.74 when applied to a test set of Mediterranean diatom images. Our model performance decreases when used to detect fragments of these microfossils; it also decreases when particles are aggregated or when images are out of focus. Microfossil detection remains high when the model is used on a microscope image set of sediments from a different oceanic basin, demonstrating its potential for application in a wide range of contemporary and paleoenvironmental studies. This automated method provides a valuable tool for analyzing complex samples, particularly for rare species under-represented in training data sets.

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来源期刊
Journal of Geophysical Research: Biogeosciences
Journal of Geophysical Research: Biogeosciences Earth and Planetary Sciences-Paleontology
CiteScore
6.60
自引率
5.40%
发文量
242
期刊介绍: JGR-Biogeosciences focuses on biogeosciences of the Earth system in the past, present, and future and the extension of this research to planetary studies. The emerging field of biogeosciences spans the intellectual interface between biology and the geosciences and attempts to understand the functions of the Earth system across multiple spatial and temporal scales. Studies in biogeosciences may use multiple lines of evidence drawn from diverse fields to gain a holistic understanding of terrestrial, freshwater, and marine ecosystems and extreme environments. Specific topics within the scope of the section include process-based theoretical, experimental, and field studies of biogeochemistry, biogeophysics, atmosphere-, land-, and ocean-ecosystem interactions, biomineralization, life in extreme environments, astrobiology, microbial processes, geomicrobiology, and evolutionary geobiology
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