AutoMorphoTrack: A modular framework for quantitative analysis of organelle morphology, motility, and interactions at...

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.