SAMWOOD: An automated method to measure wood cells along growth orientation

Abstract Quantitative wood anatomy requires precise measurement of wood cells. This step is often laborious and limiting for further analysis. We introduce Samwood, a tool based on the zero-shot Segment-Anything Model to easily segment cells on microscopic images without the need for a training dataset. The reconstruction of cell files then allows for the analysis of wood along growth orientation and precise measurement of anatomical properties of the wood, such as lumen areas. We tested our pipeline on an example dataset of fossil woods featuring deformation, heterogeneous preservation, and frequent artefacts, to assess the robustness of our approach. The model achieves a precision of 0.78 and a recall of 0.80, often producing segmentation of better quality and more consistent than a human operator. This approach substantially reduces analysis time, minimizes operator bias, and provides a robust and extensible framework for large-scale anatomical studies The complete code pipeline is available at https://github.com/umr-amap/samwood. Competing Interest Statement The authors have declared no competing interest. Footnotes Funding information Programme d’Excellence I-Site, MUSE,PE24PR01, award ID: Foss-AI