cross-channel-cell-from-hoechst-cpsam

Cross-channel cell from hoechst (Cellpose-SAM)

Fine-tuned from stock cpsam_v2 (CellposeSAM), 100 epochs, file-mode training. Built for use with spaCR.

Data

2578 training fields (237957 annotated objects), 451 held-out test fields (45098 objects); 451 test fields contained objects and were scored. Split by well, so no well appears on both sides. Per-field split and counts: training/fields.csv.

Results (held-out, best epoch 70)

F1 improves 0.5685 over stock (2.89x). The mAP ratio is inflated by a near-zero stock denominator (0.0575) - quote the delta, not the ratio.

Installation

This model is trained and used with spaCR, a toolkit for spatial phenotype analysis of high-content microscopy screens.

pip install spacr                      # https://pypi.org/project/spacr/
conda install -c conda-forge spacr     # https://anaconda.org/conda-forge/spacr

• Source: https://github.com/EinarOlafsson/spacr
• PyPI: https://pypi.org/project/spacr/
• conda-forge: https://anaconda.org/conda-forge/spacr

Get the weights

from huggingface_hub import hf_hub_download
w = hf_hub_download("einarolafsson/cross-channel-cell-from-hoechst-cpsam", "weights/cell_from_hoechst")

Use in spaCR

spaCR takes a path to a custom Cellpose model via the custom_model setting, so point it at the file downloaded above:

from spacr.core import preprocess_generate_masks

settings = {
    "src": "/path/to/your/plate",
    "cell_channel": 0,          # the cell-mask channel this model reads
    "nucleus_channel": 1,
    "custom_model": w,          # path from hf_hub_download above
    "preprocess": True,
    "masks": True,
}
preprocess_generate_masks(settings)

One caveat worth knowing: pathogen_model is not a free-form path. spaCR validates it against a fixed list (['toxo_pv_lumen', 'toxo_cyto']), so a custom model on disk goes through custom_model, not pathogen_model. See spacr/settings.py and spacr/core.py.

Use with Cellpose directly

from cellpose import models
m = models.CellposeModel(gpu=True, pretrained_model=w)
masks = m.eval(img, normalize=True)[0]

Always pass a real path. Cellpose silently substitutes its own default model for an unrecognised bare name, which would quietly give you the wrong weights.

Environment

Contents

• weights/ - final and best
• checkpoints/ - epoch snapshots
• qc/ - per-image and per-IoU metrics, per-tag summaries, comparison_vs_stock.csv
• training/ - loss_per_epoch.csv (per-epoch train and validation loss), metrics.csv (per-checkpoint F1/precision/recall/mAP/AJI/Dice), curves, fields.csv, report.json

Metrics are at IoU 0.50-0.95 in 0.05 steps; every model is scored against stock cpsam_v2 on the identical holdout so "better than stock" is always answerable.

Training data: einarolafsson/cross-channel-cell-from-hoechst