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Filtering by: Creator Curlis, JD Remove constraint Creator: Curlis, JD Discipline Engineering Remove constraint Discipline: Engineering

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  • Batch-Mask: An automated Mask R-CNN workflow to isolate non-standard biological specimens for color pattern analysis (Data set)

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    Creator:
    Curlis, JD, Renney, TJ, Davis Rabosky, AR, and Moore, TY
    Description:
    Efficient comparisons of biological color patterns are critical for understanding the mechanisms by which organisms evolve in ecosystems, including sexual selection, predator-prey interactions, and thermoregulation. However, elongate or spiral-shaped organisms do not conform to the standard orientation and photographic techniques required for automated analysis. Currently, large-scale color analysis of elongate animals requires time-consuming manual landmarking, which reduces their representation in coloration research despite their ecological importance. We present Batch-Mask: an automated and customizable workflow to facilitate the analysis of large photographic data sets of non-standard biological subjects. First, we present a user guide to run an open-source region-based convolutional neural network with fine-tuned weights for identifying and isolating a biological subject from a background (masking). Then, we demonstrate how to combine masking with existing manual visual analysis tools into a single streamlined, automated workflow for comparing color patterns across images. Batch-Mask was 60x faster than manual landmarking, produced masks that correctly identified 96% of all snake pixels, and produced pattern energy results that were not significantly different from the manually landmarked data set. The fine-tuned weights for the masking neural network, user guide, and automated workflow substantially decrease the amount of time and attention required to quantitatively analyze non-standard biological subjects. By using these tools, biologists will be able to compare color, pattern, and shape differences in large data sets that include significant morphological variation in elongate body forms. This advance will be especially valuable for comparative analyses of natural history collections, and through automation can greatly expand the scale of space, time, or taxonomic breadth across which color variation can be quantitatively examined.
    Keyword:
    convolutional neural network, photography, sensory ecology, color evolution, vision, and image segmentation
    Citation to related publication:
    Curlis, Renney, Davis Rabosky, Moore (submitted) Batch-Mask: An automated Mask R-CNN workflow to isolate non-standard biological specimens for color pattern analysis.
    Discipline:
    Engineering and Science