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DeepBryo: A web app for AI‐assisted morphometric characterization of cheilostome bryozoans
Di Martino, Emanuela Berning, Björn Gordon, Dennis P Kuklinski, Piotr Liow, Lee Hsiang Ramsfjell, Mali H Ribeiro, Henrique L Smith, Abigail M Taylor, Paul D Voje, Kjetil L
Di Martino, Emanuela
Berning, Björn
Gordon, Dennis P
Kuklinski, Piotr
Liow, Lee Hsiang
Ramsfjell, Mali H
Ribeiro, Henrique L
Smith, Abigail M
Taylor, Paul D
Voje, Kjetil L
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2023-07-04
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Abstract: Bryozoans are becoming an increasingly popular study system in macroevolutionary, ecological, and paleobiological research. Members of this colonial invertebrate phylum display an exceptional degree of division of labor in the form of specialized modules, which allows for the inference of individual allocation of resources to reproduction, defense, and growth using simple morphometric tools. However, morphometric characterizations of bryozoans are notoriously labored. Here, we introduce DeepBryo, a web application for deep‐learning‐based morphometric characterization of cheilostome bryozoans. DeepBryo is capable of detecting objects belonging to six classes and outputting 14 morphological shape measurements for each object. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. DeepBryo was trained and validated on a total of 72,412 structures in six different object classes from images of 109 different families of cheilostome bryozoans. The model shows high (> 0.8) recall and precision for zooid‐level structures. Its misclassification rate is low (~ 4%) and largely concentrated in two object classes. The model's estimated structure‐level area, height, and width measurements are statistically indistinguishable from those obtained via manual annotation. DeepBryo reduces the person‐hours required for characterizing individual colonies to less than 1% of the time required for manual annotation. Our results indicate that DeepBryo enables cost‐, labor,‐ and time‐efficient morphometric characterization of cheilostome bryozoans. DeepBryo can greatly increase the scale of macroevolutionary, ecological, taxonomic, and paleobiological analyses, as well as the accessibility of deep‐learning tools for this emerging model system.
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Di Martino, E. et al. (2023) ‘deepbryo: A web app for a‐assisted morphometric characterization of cheilostome bryozoans’, Limnology and Oceanography: Methods, 21(9), pp. 542–551. doi:10.1002/lom3.10563.
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Copyright © 2023 The Authors. Limnology and Oceanography: Methods published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. The linked file is the published version of the article.
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1541-5856
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1541-5856
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openAccess