What’s New?

CellColoc changelog

See here for a detailed list of changes made in each release of CellColoc. Please, also refer to the Repository Releases page.

Each release is also archived on Zenodo for long-term preservation and citation purposes:

🚀 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

🚀 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

---

🚀 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

---

🚀 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

---

🚀 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]

---