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AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction
Swedish Museum of Natural History, Department of Bioinformatics and Genetics.ORCID iD: 0000-0002-9384-8099
Yale University.
Yale University.
Harvard University.
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2017 (English)In: Methods in Ecology and Evolution, E-ISSN 2041-210XArticle in journal (Refereed) Published
Abstract [en]
  1. Large-scale, comparative studies of morphological variation are rare due to the time-intensive nature of shape quantification. This data gap is important to address, as intraspecific and interspecific morphological variation underpins and reflects ecological and evolutionary processes.

  2. Here, we detail a novel software package, AutoMorph, for high-throughput object and shape extraction. AutoMorph can batch image many types of organisms (e.g. foraminifera, molluscs and fish teeth), allowing for rapid generation of assemblage- scale morphological data.

  3. We used AutoMorph to image and generate 2D and 3D morphological data for >100,000 marine microfossils in about a year. Our collaborators have used AutoMorph to process >12,000 patellogastropod shells and >50,000 fish teeth.

  4. AutoMorph allows users to rapidly produce large amounts of morphological data, facilitating community-scale evolutionary and ecological studies. To hasten the adoption of automated approaches, we have made AutoMorph freely available and open source. AutoMorph runs on all UNIX-like systems; future versions will run across all platforms. 

Place, publisher, year, edition, pages
2017.
Keywords [en]
automated data extraction, bivalves, foraminifera, geometric morphometrics, ichthyoliths, macrofossils, microfossils, patellogastropoda, photogrammetry, virtual palaeontology
National Category
Bioinformatics (Computational Biology) Evolutionary Biology
Research subject
Diversity of life
Identifiers
URN: urn:nbn:se:nrm:diva-2636DOI: 10.1111/2041-210X.12915OAI: oai:DiVA.org:nrm-2636DiVA, id: diva2:1163962
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2024-01-17Bibliographically approved

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