cellmap-analyze 0.1.3

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.

---

Features

Processing Tools

Tool	CLI Command	Description
Connected Components	connected-components	Threshold and mask segmentations.
Clean Components	clean-connected-components	Refine and clean existing segmentations.
Contact Sites	contact-sites	Identify object contact regions with configurable contact distance.
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
Watershed Segmentation	watershed-segmentation	Watershed a segmentation; currently only works by doing watershed globally, but distance transform and seed finding work blockwise
Morphological Operations	morphological-operations	Allowe for erosion and dilation of a segmented dataset, but since ordering of processing may matter, there is no guarantee of consistency based on order of blocks processed

Analysis Tools

Tool	CLI Command	Description
Measurement	measure	Compute metrics (volume, surface area) for objects and contact sites.
Fit Lines	fit_lines_to_segmentations	Fit geometric lines to elongated/cylindrical structures.
Assign to Cells	assign_to_cells	Map segmented objects to cells based on centers of mass.

---

Installation

Install via PyPI:

pip install cellmap-analyze

---

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.

---

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: 12000000000
    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

---

Acknowledgements

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