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priggelab/
bluespotter-lc

Name: bluespotter-lc
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BlueSpotter — LC-optimized Cellpose-SAM

Fine-tuned Cellpose-SAM weights for instance segmentation of locus coeruleus (LC) neurons in neuromelanin / histology microscopy slices. Part of the BlueSpotter project (priggelab.com): a standardized, hardware-independent pipeline for quantitative LC analysis.

What this is

Base model: Cellpose-SAM (cpsam).
Task: instance segmentation — each detected neuron gets a unique integer label.
Trained on: paired image/mask crops from neuromelanin slice cohorts (see the BlueSpotter repo's data convention and manifest).

Usage

python

from huggingface_hub import hf_hub_download
from cellpose import models

# Download the fine-tuned weights from this repo
weights = hf_hub_download(repo_id="TeamPrigge/bluespotter-lc", filename="bluespotter_lc")

# Load exactly like the base Cellpose-SAM model, but with these weights
model = models.CellposeModel(gpu=True, pretrained_model=weights)

# Segment — masks is a label image; masks.max() == number of neurons found
masks, flows, styles = model.eval(image)
n_neurons = int(masks.max())

The inference call matches BlueSpotter's training-time QC (src/bluespotter/viz.py), so masks here are directly comparable to the validation overlays produced during training.

Intended use & limitations

Intended: research-grade segmentation of LC neurons in similar imaging modalities to the training data.
Out of scope: clinical/diagnostic use; cell types or modalities far from the training distribution. Always visually QC predictions before quantifying.

Training

Fine-tuned via the BlueSpotter Colab workflow. Hyperparameters and per-epoch losses for each run are tracked in MLflow; see params.yaml and docs/SETUP.md in the code repository.

Citation

If you use these weights, please cite Cellpose-SAM and credit the BlueSpotter project (priggelab.com).

License note: cc-by-nc-4.0 is a placeholder default for non-commercial research sharing — confirm or change it before publishing.

priggelab/bluespotter-lc