Artificial intelligence to bring museum specimens to the masses
New method proposed by scientists could drastically improve the time it
takes to extract information from museum specimens.
Date:
March 24, 2022
Source:
Cardiff University
Summary:
Scientists are using cutting-edge artificial intelligence to
help extract complex information from large collections of museum
specimens.
FULL STORY ========================================================================== Scientists are using cutting-edge artificial intelligence to help extract complex information from large collections of museum specimens.
==========================================================================
A team from Cardiff University is using state-of-the-art techniques to automatically segment and capture information from museum specimens
and perform important data quality improvement without the need of
human input.
They have been working with museums from across Europe, including the
Natural History Museum, London, to refine and validate their new methods
and contribute to the mammoth task of digitising hundreds of millions
of specimens.
With more than 3 billion biological and geological specimens curated
in natural history museums around the world, the digitization of museum specimens, in which physical information from a particular specimen is transformed into a digital format, has become an increasingly important
task for museums as they adapt to an increasingly digital world.
A treasure trove of digital information is invaluable for scientists
trying to model the past, present and future of organisms and our planet,
and could be key to tackling some of the biggest societal challenges
our world faces today, from conserving biodiversity and tackling climate
change to finding new ways to cope with emerging diseases like COVID-19.
The digitization process also helps to reduce the amount of manual
handling of specimens, many of which are very delicate and prone to
damage. Having suitable data and images available online can reduce
the risk to the physical collection and protect specimens for future generations.
==========================================================================
In a new paper published today in the journal Machine Vision and
Applications, the team from Cardiff University has taken a step towards
making this process cheaper and quicker.
"This new approach could transform our digitization workflows," said
Laurence Livermore, Deputy Digital Programme Manager at the Natural
History Museum, London.
The team has created and tested a new method called image segmentation,
that can easily and automatically locate and bound different visual
regions on images as diverse as microscope slides or herbarium sheets
with a high degree of accuracy.
Automatic segmentation can be used to focus the capturing of information
from specific regions of a slide or sheet, such as one or more of the
labels stuck on to the slide. It can also help to perform important
quality control on the images to ensure that digital copies of specimens
are as accurate as they can be.
"In the past, our digitization has been limited by the rate at which we
can manually check, extract, and interpret data from our images. This
new approach would allow us to scale up some of the slowest parts of
our digitzation workflows and make crucial data more readily available
to climate change and biodiversity researchers," continued Livermore.
==========================================================================
The method has been trained and then tested on thousands of images of microscope slides and herbarium sheets from different natural history collections, demonstrating the adaptability and flexibility of the system.
Included in the images is key information about the microscope slide or herbarium sheet, such as the specimen itself, labels, barcodes, colour
charts, and institution names.
Typically, once an image has been captured it then needs to be checked for quality control purposes and the information from the labels recorded --
a process that is currently done manually, which can take up a lot of
time and resource.
Lead author of the new study Professor Paul Rosin, from Cardiff
University's School of Computer Science and Informatics, said: "Previous attempts at image segmentation of microscope slides and herbarium sheets
have been limited to images from just a single collection.
"Our work has drawn on the multiple partners in our large European project
to create a dataset containing examples from multiple institutions and
shows how well our artificial intelligence methods can be trained to
process images from a wide range of collections.
"We're confident that this method could help improve the workflows of
staff working with natural history collections to drastically speed up
the process of digitization in return for very little cost and resource." Microscope slides were provided by Natural History Museum, Royal Botanic Gardens, Kew and Naturalis Biodiversity Center, whilst herbarium sheets
were provided by National Museum Wales, Muse'um National d'Histoire
Naturelle, Museum fu"r Naturkunde, Finnish Museum of Natural History,
Meise Botanic Garden, Natural History Museum, and Naturalis Biodiversity Center.
========================================================================== Story Source: Materials provided by Cardiff_University. Note: Content
may be edited for style and length.
========================================================================== Journal Reference:
1. Abraham Nieva de la Hidalga, Paul L. Rosin, Xianfang Sun, Laurence
Livermore, James Durrant, James Turner, Mathias Dillen,
Alicia Musson, Sarah Phillips, Quentin Groom & Alex
Hardisty. Cross-validation of a semantic segmentation network for
natural history collection specimens.
Machine Vision and Applications, 2022 DOI:
10.1007/s00138-022-01276-z ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/03/220324104448.htm
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