Open Chrono-Morph Viewer: visualize big bioimage time series containing heterogeneous volumes

Andre C Faubert ¹, Shang Wang ¹

⁰Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.

Bioinformatics (Oxford, England) +

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January 15, 2025

View abstract on PubMed

Summary

Open Chrono-Morph Viewer (OCMV) handles large, complex biological imaging datasets. This new software enables efficient visualization of multi-timescale, heterogeneous data for research.

Area Of Science

Bioimaging and Computational Biology
Scientific Software Development

Background

Time-lapse 3D imaging is crucial for biological process studies but demands software capable of managing terabytes of voxel data.
Existing multidimensional viewers often struggle with heterogeneous voxel counts, datatypes, and modalities within a single timeline.

Purpose Of The Study

To introduce Open Chrono-Morph Viewer (OCMV), a software solution for efficient investigation of complex biological imaging datasets.
To provide a user-friendly interface for handling multi-timescale and heterogeneous data, overcoming limitations of current tools.

Main Methods

Development of OCMV with a straightforward graphical user interface (GUI).
Utilizes the common NRRD format for compatibility and supports separate volume files for multi-timescale datasets.
Incorporates dynamic clipping surfaces for 3D morphology investigation and a scriptable animation API for reproducible visualization.

Main Results

OCMV facilitates rapid investigation of large, multi-timescale biological datasets.
The software supports heterogeneous voxel counts, datatypes, and modalities in a unified timeline.
Enables quantitative, repeatable, and publication-quality visualizations through its animation API.

Conclusions

OCMV offers a robust and accessible solution for advanced bioimaging data analysis.
Its design promotes community-driven development through implementation in pure Python with common libraries.
The software enhances the study of biological processes by simplifying the handling and visualization of complex imaging data.