A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics.

ABSTRACT: Geometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. We combine techniques from image registration, geometric morphometrics, and deep learning to automate and optimize anatomical landmark detection. We test our method on high-resolution, micro-computed tomography images of adult mouse skulls. To ensure generalizability, we use a morphologically diverse sample and implement

SUBMITTER: Devine J 

PROVIDER: S-EPMC7880197 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature