cellmap-analyze 0.4.0

Code to perform analysis on segmentations like those produced by CellMap

cellmap-analyze

A suite of Dask-powered tools for processing and analyzing terabyte-scale 3D segmentation datasets. Supports both isotropic and anisotropic voxel sizes.

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Features

Processing Tools

Tool	CLI Command	Description
Connected Components	connected-components	Threshold predictions, apply masks, and extract connected components. Volume thresholds are in physical units (nm³).
Clean Components	clean-connected-components	Refine existing segmentations by removing small/large components.
Contact Sites	contact-sites	Identify regions where two segmentations are within a configurable physical distance. Handles mismatched voxel sizes by resampling to a common resolution.
Fill Holes	fill-holes	Fill interior gaps in segmented volumes.
Filter IDs	filter-ids	Exclude unwanted segmentation IDs.
Mutex Watershed	mws	Mutex watershed agglomeration from affinities.
Label With Mask	label-with-mask	Label one dataset with IDs from another.
Morphological Operations	morphological-operations	Erosion and dilation of segmented datasets. Processing order across blocks is not guaranteed.
Skeletonize	skeletonize	Generate skeletons from segmented objects with optional pruning and simplification. Automatically resamples to isotropic resolution before skeletonization.

Analysis Tools

Tool	CLI Command	Description
Measurement	measure	Compute metrics (volume, surface area, radius of gyration, bounding box) for objects and contact sites. Supports raw intensity statistics when a raw dataset is provided.
Fit Lines	fit_lines_to_segmentations	Fit geometric lines to elongated/cylindrical structures.
Assign to Organelles	assign_to_organelles	Map segmented objects to organelles based on centers of mass.

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Anisotropic data

All operations handle anisotropic voxel sizes (e.g. (8, 8, 32) nm in ZYX). Physical-unit parameters like minimum_volume_nm_3, contact_distance_nm, and gaussian_smoothing_sigma_nm are automatically converted to the appropriate per-axis voxel units. When two datasets have different voxel sizes, they are resampled to a common resolution using nearest-neighbor interpolation.

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Installation

pip install cellmap-analyze

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Usage

All commands share the same basic interface:

<command> [options] <config_path>

• <command>: One of the processing or analysis tools listed above.

• <config_path>: Directory containing:

  • run-config.yaml (parameters for your chosen command)
  • dask-config.yaml (Dask cluster settings)

Options:

• -n, --num-workers N: Number of Dask workers to launch.

Output: A new directory named config_path-<YYYYMMDDHHMMSS> will be created, containing copies of your configs and an output.log for monitoring.

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Configuration Examples

The following run-config.yaml could be used to run connected-components.

run-config.yaml

input_path: /path/to/predictions.zarr/mito/s0
output_path: /path/to/segmentations.zarr/mito
intensity_threshold_minimum: 0.71
minimum_volume_nm_3: 1E7
delete_tmp: true
connectivity: 1
mask_config:
  cell:
    path: /path/to/masks.zarr/cell/s0
    mask_type: inclusive
fill_holes: true

dask-config.yaml

The following dask-config.yaml files can be used for a variety of tasks.

Local

jobqueue:
  local:
    ncpus: 1
    processes: 1
    cores: 1
    log-directory: job-logs
    name: dask-worker

distributed:
  scheduler:
    work-stealing: true

LSF Cluster

jobqueue:
  lsf:
    ncpus: 8        # cores per job chunk
    processes: 12  # worker processes per chunk
    cores: 12      # threads per process (1 thread each)
    memory: 120GB  # 15 GB per slot
    walltime: 08:00
    mem: 120000000000
    use-stdin: true
    log-directory: job-logs
    name: cellmap-analyze
    project: charge_group

distributed:
  scheduler:
    work-stealing: true
  admin:
    log-format: '[%(asctime)s] %(levelname)s %(message)s'
    tick:
      interval: 20ms
      limit: 3h

Submission

To run on 12 dask workers:

Local run example:

connected-components -n 12 config_path

Cluster submit example (LSF):

bsub -n 4 -P chargegroup connected-components -n 12 config_path

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Acknowledgements

The center-finding implementation is taken from funlib.evaluate.