v0.0.5

🚀 CellColoc v0.0.5

June 24, 2026

This release extends the multi-channel colocalization output with channel-wise morphology tables and ROI-level morphology summaries so that colocalization results and per-channel object properties can be inspected together in one workbook (Excel-file).

✨ Features

• extend the multi-channel colocalization export with channel-wise morphology
  tables and per-ROI morphology summaries:
  • augment cell_summary with cell-channel size and shape metrics
  • add marker_properties for segmented marker objects
  • add 3rd_channel_properties when an optional third channel is analyzed
  • rename the ROI overview export sheet to roi_coloc_overview
  • add roi_cell_summary, roi_marker_summary, and optional
    roi_3rd_channel_summary sheets with per-ROI mean morphology metrics

v0.0.4

🚀 CellColoc v0.0.4

June 23, 2026

This release expands CellColoc from a pure multi-channel colocalization
workflow into a broader interactive microscopy-analysis toolkit by adding a
dedicated single-channel mode, a first full set of usage tutorials, and
tutorial-derived notebook counterparts for the interactive example scripts.

✨ Features

• add a dedicated single-channel segmentation and counting workflow that can
  analyze one microscopy channel without any colocalization step while still
  reusing CellColoc's existing core capabilities:
  • Cellpose and threshold-based segmentation backends
  • ROI-based or whole-image analysis
  • prefiltering and postfiltering
  • global z-cropping and optional z projection
  • cached Cellpose refinement
  • manual napari relabeling and reanalysis
  • standardized mask and table export
• add a dedicated 2D DAPI nuclei demo script for the new single-channel
  workflow
• extend the single-channel object export with morphology metrics:
  • 2D area, perimeter, roundness, and eccentricity
  • 3D volume, voxel-surface area, sphericity, and ellipticity-like
    elongation
  • a separate voxel plausibility sheet in the Excel export
  • per-ROI averages of the new morphology metrics

📃 Changes

• allow VOXEL_SCALE_ZYX to be provided either as a full (Z, Y, X)
  tuple or, for 2D workflows, as a shorter (Y, X) tuple that is expanded
  internally to (1.0, Y, X)
• add the first full usage tutorials to the Read the Docs documentation:
  • a 2D tutorial based on the DAPI-stained nuclei example workflow
  • a 3D tutorial based on the microglia example workflow
  • a three-channel tutorial
  • a three-channel z-projection tutorial
  • a 2D single-channel nuclei tutorial
• generate notebook counterparts for the interactive example workflows from
  the tutorial structure itself, including local figure references inside the
  user_scripts folder
• expand the documentation with mathematical definitions of object-based
  colocalization and occupancy metrics
• improve the Read the Docs configuration so copy buttons are shown on all
  standard highlighted code blocks instead of only Python code snippets
• extend the 2D DAPI example user script with:
  • whole-image-as-single-ROI mode
  • automatic reuse of an existing saved ROI mask from the results directory
• add a dedicated three-channel 3D microglia demo script that demonstrates:
  • active segmentation of the third channel
  • separate visualization of cells positive for channel 0+1
  • separate visualization of cells positive for channel 0+2
  • separate visualization of cells positive for channel 0+1+2
• add a dedicated three-channel z-projection demo script that demonstrates:
  • global z projection before segmentation
  • projected three-channel analysis
  • projected positivity views for 0+1, 0+2, and 0+1+2
• extend cache-based Cellpose refinement so the optional third analysis channel
  can also be rebuilt from cached Cellpose outputs, including optional
  threshold changes and postfiltering
• keep manual reanalysis after napari label edits consistent with the active
  analysis z-bounds in the 3D workflows
• surface the new single-channel workflow explicitly in the README and the
  general documentation overview as a first-class CellColoc feature

v0.0.3

🚀 CellColoc v0.0.3

June 21, 2026

This release adds the first project-wide archival and example-data publication
records on Zenodo for CellColoc.

This release provides:

• an official Zenodo archive for CellColoc that can now be used for
  software citation:
  • DOI: 10.5281/zenodo.20787509
  • Citation: Musacchio, F. (2026). CellColoc: A Python package for
    interactive segmentation-based colocalization analysis in microscopy
    images. Zenodo. https://doi.org/10.5281/zenodo.20787509
• a dedicated Zenodo example-data record for CellColoc:
  • DOI: 10.5281/zenodo.20788293
• updated release metadata to reflect the new citable software archive and
  externally hosted example dataset

v0.0.2

🚀 CellColoc v0.0.2

June 21, 2026

This release adds the initial Read the Docs documentation structure for CellColoc.

This release provides:

• a first Sphinx / Read the Docs documentation scaffold under docs/
• initial documentation pages for:
  • project overview
  • installation
  • usage landing page
  • API reference
  • changelog
• automatic API-reference structuring based on the public cellcoloc package
• a prepared usage section that will later be expanded with dedicated
  user-script walkthroughs

Notes:

• the detailed user-script usage pages are intentionally still pending and will
  follow in a later documentation update

v0.0.1

🚀 CellColoc v0.0.1

June 21, 2026

First public main release of CellColoc.

This initial release provides:

• the reusable cellcoloc Python package for interactive, segmentation-based colocalization analysis in microscopy images
• stepwise user-script workflows for VS Code interactive window and notebook-like execution
• OMIO-based microscopy loading with TZCYX handling
• automatic 2D versus 3D detection from the raw z dimension
• voxel-size resolution from explicit user input or OMIO metadata, with fallback to (1.0, 1.0, 1.0) when necessary
• channel-wise segmentation method selection with support for:
  • cellpose
  • otsu
  • li
  • percentile
• optional ROI drawing in napari
• optional whole-image analysis as one single ROI
• optional reuse of previously saved ROI masks
• per-cell overlap analysis and marker-positivity classification
• standardized detailed, summary, and overview result tables
• standardized export into a results/ subfolder next to the raw dataset
• occupancy metrics for every segmented channel
• optional third-channel segmentation and occupancy quantification
• optional third-channel cell-positivity analysis and double-positive reporting
• optional global z cropping for internal analysis
• optional global z projection using:
  • max
  • mean
  • median
  • std
  • var
• optional anisotropy handling for true 3D Cellpose runs
• optional flow3d_smooth support for Cellpose
• optional image prefiltering with:
  • gaussian
  • median
  • laplacian_of_gaussian
  • ordered prefilter chains
• optional label postfiltering with:
  • min_intensity
  • local_contrast
  • bright_pixel_support
  • ordered postfilter chains
• fast Cellpose cache-based refinement using stored network outputs
• optional manual napari mask editing followed by table recomputation
• reusable visualization helpers with selective layer refreshing in napari
• runtime fallback handling for cache and config directories when desktop libraries cannot write to default locations
• packaging metadata for installation via pip

Packaging notes:

• PyPI package name: cellcoloc
• import name: cellcoloc
• optional interactive extra: cellcoloc[interactive]
• optional tested Cellpose 3 extra: cellcoloc[cellpose3]