DeepBryo: A web app for AI-assisted morphometric characterization of cheilostome bryozoans

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.