RootScope: Cross-species Root Cell-Type Classification from Confocal Microscopy Images

Trained model weights for RootScope.

RootScope takes a raw confocal root-tip cross-section TIFF, segments every cell with Cellpose-SAM, describes each cell with hand-crafted morpho-topological features plus fine-tuned DINOv2 embeddings, and classifies it into one of nine anatomical cell types using an iterative tree-based ensemble.

Install

git clone https://github.com/ct-tranchau/Rootscope.git
cd Rootscope
conda env create -f environment.yml
conda activate rootscope
pip install .

Run

rootscope --tif my_image.tif --out results/

results/ gets a per-cell CSV and a labeled overlay PNG, per model plus the ensemble.

Or from Python:

from rootscope import predict_tif
df = predict_tif("my_image.tif", out_dir="results/")

Cell types

root_cap · epidermis · exodermis · cortex · endodermis · pericycle · stele · xylem · phloem

Files

File	Size
model_RandomForest.joblib	349 MB
backbone.pt (fine-tuned DINOv2)	88 MB
model_LightGBM.joblib	29 MB
model_XGBoost.joblib	14 MB
scalers, feature columns, label encoder	small

Performance

Held-out test accuracy, 480 features, 9 classes:

Model	Test
LightGBM	0.965
XGBoost	0.959
RandomForest	0.937

Inference: ~1.5–3 min per image on a single GPU.

Notes

• Set --um-per-px to your image's real scale — it is not auto-detected, and the default of 1.0 distorts every size feature.
• Input must be a raw image, not a segmentation mask.
• CPU works but is far slower (~15–30 min per image).
• scikit-learn is pinned to 1.7.2, the version these models were saved with.

Contact

tnchau@vt.edu