LizardMorph: A generalizable machine learning framework for automated anatomical landmark detection in digital images
Morphological measurements underpin a wide range of ecological and evolutionary research, yet the manual landmarking workflows on which most morphometric studies depend remain a persistent bottleneck that limits both the pace and scale of biological research. Machine learning offers compelling solutions, but most automated landmarking tools require substantial computational expertise, creating a gap between technical capability and practical adoption by biologists. Here, we present LizardMorph, an integrated machine learning pipeline and web-based interface for semi-automated anatomical landmark detection on biological images. LizardMorph couples a fine-tuned ML-Morph shape predictor with an accessible, browser-based interface that enables researchers to upload images, review automated landmark predictions, interactively correct outliers through point-and-click editing, and export results in standard morphometric formats--all without programming expertise or local software installation. Using dorsal X-ray radiographs of Anolis lizards with 34 anatomical landmarks as a proof-of-concept, we show that the ML-Morph model achieves high predictive accuracy, with landmarks on well-defined skeletal structures predicted with 100% accuracy within a 1 mm tolerance threshold. A controlled user study comparing LizardMorph against traditional manual landmarking (TpsDig2) demonstrated significant efficiency gains: experienced annotators completed LizardMorph landmark verification 37.5% faster than manual annotation. Extrapolated to batch processing 1,000 lizards, LizardMorph saves experienced researchers approximately 6.5 hours of manual processing time. Critically, LizardMorph implements a human-in-the-loop design in which automated predictions serve as editable starting points, preserving researcher oversight and enabling correction of the occasional large-error outliers that would be unacceptable in fully automated workflows. LizardMorph is freely available as an open-source tool and provides a replicable framework for developing ML-assisted annotation tools that can democratize access to high-quality morphometric analysis across diverse biological research communities.