Abstract
Feather growth patterns are important anatomical phenotypes for investigating the underlying genomic regulation of skin and epidermal appendage development. However, characterization of feather growth patterns previously relied on manual examination and visual inspection, which are both subjective and practically prohibitive for large sample sizes. Here, we report a new high-throughput technique to quantify the location and spatial extent of reversed feathers that comprise head crests in domestic pigeons. Phenotypic variation in pigeon feather growth patterns were rendered by computed tomography (CT) scans as point clouds. We then developed machine learning based, feature extraction techniques to isolate the feathers, and map the growth patterns on the skin in a quantitative, automated, and non-invasive way. Results from five test animals were in excellent agreement with "ground truth" results obtained via visual inspection, which demonstrates the viability of this method for quantification of feather growth patterns. Our findings underscore the potential and increasingly indispensable role of modern computer vision and machine learning techniques at the interface of organismal biology and genetics.