AutoMorphoTrack: A modular framework for quantitative analysis of organelle morphology, motility, and interactions at single-cell resolution

Quantitative imaging of organelle dynamics provides crucial insights into cellular function, state, and organization; however, existing analysis workflows often require advanced coding expertise and multiple software tools. AutoMorphoTrack is an open-source Python toolkit that automates organelle detection, morphology classification, motility tracking, and colocalization from multichannel fluorescence microscopy image stacks. The platform includes adaptive segmentation, organelle trajectory reconstruction, and pixel-level overlap quantification within a unified, reproducible framework that can be executed as an interactive Jupyter notebook, a modular Python package, or through AI-assisted natural-language commands. Each analysis step outputs publication-ready images, time-lapse videos, and standardized quantitative data tables. To complement the main pipeline, an accompanying script—AMTComparison.py—is provided to demonstrate how AutoMorphoTrack’s outputs can be extended for comparative analysis across individual neurons or experimental conditions. Together, these tools provide an accessible and framework for high-content, reproducible quantification of subcellular morphology, motility, and interactions at single-cell resolution.