ALIGN: A modular framework for quantitative analysis of longitudinal development in Caenorhabditis elegans

ALIGN enables accurate, end-to-end longitudinal analysis of Caenorhabditis elegans, including segmentation across heterogeneous imaging, stage-transition detection, and motion-artefact removal via computational straightening.

• Why it matters: Reproducible, scalable quantification of large longitudinal live-imaging datasets is a bottleneck, limiting growth and behaviour studies.
• What they did: The modular pipeline combines deep learning segmentation, automated quality control, computational straightening, feature extraction, and interactive annotation, and it is configurable for end-to-end use without coding.
• The result: In wild-type and eat-2 mutants, ALIGN measures pharyngeal pumping and growth rates and uncovers a non-proportional relationship between feeding and growth, supporting consistent single-animal developmental analysis.

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