气候变化领域集成服务门户(CCPortal): Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow

CCPortal

DOI	10.5194/cp-16-2415-2020
	Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow
	Tetard M.; Marchant R.; Cortese G.; Gally Y.; De Garidel-Thoron T.; Beaufort L.
发表日期	2020
ISSN	1814-9324
起始页码	635
结束页码	651
卷号	16 期号:6
英文摘要	Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysis of long-term, radiolarian-based palaeoclimatic records from siliceous-remnant-bearing samples. © 2020 Author(s).
来源期刊	Climate of the Past
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/183632
作者单位	Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France; GNS Science, Lower Hutt, New Zealand; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia
推荐引用方式 GB/T 7714	Tetard M.,Marchant R.,Cortese G.,et al. Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow[J],2020,16(6).
APA	Tetard M.,Marchant R.,Cortese G.,Gally Y.,De Garidel-Thoron T.,&Beaufort L..(2020).Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow.Climate of the Past,16(6).
MLA	Tetard M.,et al."Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow".Climate of the Past 16.6(2020).