metadata
license: mit
tags:
- biology
- plant-biology
- microscopy
- image-segmentation
- cell-type-classification
- cellpose
- dinov2
library_name: rootscope
pipeline_tag: image-classification
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-pxto 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-learnis pinned to 1.7.2, the version these models were saved with.