SegmentR: Deep learning for automated segmentation with an R interface

The increasing digitization of biological data has generated biodiversity data at an unprecedented scale. However, extracting phenotypic information from these images poses unique challenges for biologists. Manual image segmentation is time-consuming and can be subjective, while existing automated solutions often require extensive coding experience or utilize coding languages not typically used by practicing ecologists and evolutionary biologists. Here, I present SegmentR, a user-friendly software package that leverages two state-of-the-art deep learning models – GroundinDINO and an efficient version of the Segment Anything Model (SAM). The SegmentR package provides an R-based interface, making it more accessible to biologists without coding experience. SegmentR allows users to load images, automatically segment them based on text prompts, and extract regions of interest for downstream analysis. The package includes basic visualization and data processing functions to facilitate interpretation of the results and integration with existing analytical workflows. This paper introduces SegmentR's features and demonstrates its utility through examples including isolating fish anatomy, batch processing flower images for color analysis, and segmenting museum specimens.