Cell segmentation metrics
Binary, multiclass and instance segmentation metrics using NumPy and SciPy. PyTorch and slide-reading packages are not required.
From a source checkout, install this package independently:
python -m pip install ./packages/metrics
Compare integer instance masks, where zero is background and positive values identify separate nuclei:
import numpy as np
from cellseg_metrics import panoptic_quality
reference = np.array([[0, 1, 1, 0, 2, 2]])
prediction = np.array([[0, 7, 7, 0, 0, 0]])
scores = panoptic_quality(reference, prediction)
print(scores)
pq measures instance segmentation quality, dq measures detection quality,
and sq measures overlap quality for matched instances. Instance IDs do not
need to match between the masks.
The full cellseg-models-pytorch library preserves its existing
cellseg_models_pytorch.metrics imports and uses this same implementation.
See the metrics reference
for inputs, outputs and empty-mask behavior.
1 maintainer
Metadata
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