einarolafsson/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)

model	F1	precision	recall	mAP	AJI	Dice
stock cpsam_v2	0.3012	0.3086	0.2942	0.0575	0.3506	0.5235
best	0.8697	0.9437	0.8064	0.5591	0.7991	0.8948
final	0.8667	0.9429	0.8018	0.552	0.7929	0.8904

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

cellpose (training)	4.0.9
cellpose (scoring)	4.0.9
GPU	NVIDIA GeForce RTX 3090 Ti
base weights	cpsam_v2

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