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 are notable for displaying an exceptional degree of division of labor in the form of specialized modules (polymorphs), which allow 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, due to the high number of structures often captured per image, as well as the need for specialized knowledge necessary for classifying individual skeletal structures within those images. We here introduce DeepBryo, a web application for deep learning-based morphometric characterization of cheilostome bryozoans. DeepBryo requires a single image as input and performs measurements automatically using instance segmentation algorithms. DeepBryo is capable of detecting objects belonging to six classes and outputting fourteen morphological shape measurements for each object based on the inferred segmentation maps. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. Measurements can then be downloaded as a comma-separated values file. DeepBryo has been trained and validated on a total of 72,412 structures, belonging to six different object classes in 935 SEM images of cheilostome bryozoans belonging to 109 different families. The model shows high (>0.8) recall and precision for zooid-level structures. Its misclassification rate is low (~4%) and largely concentrated in a single object class (opesia). The model's estimated structure-level area, height, and width measurements are statistically indistinguishable from those obtained via manual annotation (r2 varying from 0.89 to 0.98) and show no detectable bias. DeepBryo reduces the person-hours required for characterizing the zooids in individual colonies to less than 1% of the time required for manual annotation at no significant loss of measurement accuracy. 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. Finally, DeepBryo provides the building blocks necessary for adapting the current application to other study groups.