Automated collection of single species of microfossils using a deep learning–micromanipulator system

Abstract

For geochemical analysis such as stable isotope ratio, radiocarbon dating and minor element analysis for a single species of microfossils, a large number of specimens, is required. Collecting specimens one by one under a microscope requires enormous time and effort. In this study, we developed a device that automates these efforts and can be used without expert knowledge. Microfossils can be accurately classified and identified to taxonomic species level using deep learning, which is one of the learning methods of artificial intelligence (AI), and picked up using a micromanipulator installed in the microscope with an automated motorized X-Y stage. A prototype of the classification model AI-PIC_20181024 showed the ability to classify microfossil species Cycladophora davisiana and Actinomma boreale (radiolarians) with accuracy exceeding 90% at a confidence level > 0.90. Using this method, it is possible to collect a large number of particles with speed and accuracy that cannot be achieved by a human technician. Although this technology can only be used for specific species of microfossils, it greatly reduces the hand work of picking and also enables chemical analysis, such as isotope ratio and minor element analysis, for small microfossil species for which it had been difficult to collect enough specimens. In addition to microfossils, this technology can be applied to other particles, with applications expected in various fields, such as medical, food, horticulture, and materials.

Time Machine Biology: Cross-Timescale Integration of Ecology, Evolution, and Oceanography

“…Automated picking systems that take sieved size fractions, separate them into individual particles, and image each particle may greatly reduce picking times (de Garidel-Thoron et al, 2017;Itaki et al, 2020). With samples that have already been picked and mounted, hundreds or thousands of individual microfossils can be imaged simultaneously in three dimensions and algorithmically parsed into individual images from which basic morphometric traits and features can be automatically extracted and ana-lyzed at the assemblage scale (Beaufort et al, 2014;Elder et al, 2018;Hsiang et al, 2018Hsiang et al, , 2019Kahanamoku et al, 2018).…”

Automated Detection of Paleoenvironmental Proxy, Eucampia Index, in a Microscopic Slide Using a Convolutional Neural Network System

“…Images of all particles including E. antarctica valves in the prepared slides were captured using the Image Collection Unit of the miCRAD system described in Itaki et al (2020). The Image Collection Unit, which is based on "Collection Pro" from Micro Support Co., Ltd., automatically acquires digital microscopic images of particles scattered in the observation eld using an electric X-Y stage microscope controlled by a computer.…”

“…This study revealed that a model capable of detecting the ratio of two diatom species can be constructed using the miCRAD system for the rst time. The Image Collection Unit in the miCRAD system enables researches not only to obtain cropped object images for training datasets to construct CNN models, but also to conduct automated classi cation of particle images at the same time of capturing after constructing CNN models (Itaki et al, 2020). Using the model constructed in this study, automatic detection of Eucampia Index from a diatom slide can be applied to a large-scale investigation of the index variation and geographical distribution in the Southern Ocean.…”

“…In this study, to automate detection of Eucampia Index, we employed a newly developed system, the microfossil Classi cation and Rapid Accumulation Device (miCRAD), which was used for automatic detection of radiolarians (Itaki et al, 2020). We constructed a classi cation model for identifying Eucampia valves in a diatom slide and veri ed the usefulness of the model for paleoenvironmental studies.…”

“…We constructed a classi cation model for identifying Eucampia valves in a diatom slide and veri ed the usefulness of the model for paleoenvironmental studies. The system can be used to construct deep-learning based models for classifying some species easily and rapidly by automated collecting images of particles present in a normal microscopic slide (Itaki et al, 2020). Since the resolution of the original imaging system was insu cient for classifying diatom valves to species, we increased the magni cation of the objective lens and the resolution of the charge-coupled device (CCD) camera used for microscopic observations.…”