SAMWOOD: An automated method to measure wood cells along growth orientation
Killian VerlingueiD, Guilhem BruneliD, Anne‐Laure DecombeixiD, Merlin Ramel, Paul TressoniD
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. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/711054v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@7be69borg.highwire.dtl.DTLVardef@6104d5org.highwire.dtl.DTLVardef@1844e9borg.highwire.dtl.DTLVardef@144d214_HPS_FORMAT_FIGEXP M_FIG C_FIG
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- SAMCell: Generalized Label-Free Biological Cell Segmentation with Segment Anything
Alexandra D. VandeLoo, Nathan J. Malta, … Craig R. Forest·PLoS ONEPeer-reviewed·Sep 8, 2025
- Deep Learning Based Cell Segmentation of 3D Confocal Images of Wood for Enzymatic Hydrolysis Applications
Abdelmalek Faraj, Solmaz Hossein Khani, … Yassin Refahi·bioRxiv·Oct 30, 2026
- CellSAM: A Foundation Model for Cell Segmentation
Uriah Israel, Markus Marks, … David Van Valen·bioRxiv·Nov 17, 2023
- Cellpose-SAM: superhuman generalization for cellular segmentation
Marius Pachitariu, Michael Rariden, Carsen Stringer·bioRxiv·Apr 28, 2025
- Automated methods for 3D Segmentation of Focused Ion Beam-Scanning Electron Microscopic Images
B. Caffrey, A. V. Maltsev, … S. Subramaniam·bioRxiv·Jan 1, 2019
- Accurate Automated Segmentation of Autophagic Bodies in Yeast Vacuoles Using Cellpose 2.0
E. C. Marron, J. Backues, … S. K. Backues·AutophagyPeer-reviewed·May 18, 2024