• Artificial intelligence to bring museum

    From ScienceDaily@1:317/3 to All on Thu Mar 24 22:30:44 2022
    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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