Cell segmentation — epitools.analysis.segmentation#

This module contains functions for segmenting 3D (ZYX) and 4D (TZXY) images.

epitools.analysis.segmentation.calculate_segmentation(projection, spot_sigma, outline_sigma, threshold)#

Segment an image using a thresholded local-minima-seeded watershed algorith.

This will segment cells in images with marked membranes that have a high signal intensity.

The two sigma parameters allow tuning the segmentation result. Under the hood, this algorithm first applies two Gaussian blurs, then uses a local minima detection and seeded watershed. Afterwards, all objects are removed that have an average intensity below a given threshold.

Parameters

  • projection (numpy.ndarray[Any, numpy.dtype[numpy.float64]]) – A projected image stack processed using Epitools.

  • spot_sigma (float) – Controls how close segmented cells can be - larger values result in more seeds used in the segmentation

  • outline_sigma (float) – Controls how precisely segmented cells are outlined - larger values result in more precise (rougher) cell boundaries.

  • threshold (float) – Cells with an average intensity below threshold are will be removed from the segmentation and treated as background.

Returns

np.NDArray

Seeds used in the segmentated

np.NDArray

Labels of the segmented image (0 corresponds to background)

Return type

tuple[numpy.ndarray[Any, numpy.dtype[numpy.float64]], numpy.ndarray[Any, numpy.dtype[numpy.int64]]]