Benchmarking Machine Learning and Automated Image Analysis for Organelle Quantification

Quantitative organelle analysis is highly sensitive to image-processing choices, limiting reproducibility across microscopy studies. Here, we systematically compare automated, interactive machine learning, and deep learning based pipelines for lipid droplet and mitochondrial quantification in live human osteosarcoma cells imaged by fluorescence microscopy and label-free holotomography. Using standardized downstream feature extraction, we evaluated script-based workflows (Fiji, Python), a modular platform (CellProfiler), interactive machine learning (ilastik), and pretrained deep learning models. Lipid droplet segmentation was qualitatively consistent across approaches; however, droplet counts, and size distributions varied substantially between pipelines and imaging modalities, with ilastik reducing background-driven detections and improving cross-modality agreement. In contrast, mitochondrial quantification proved highly sensitive to segmentation and skeletonization choices, particularly in holotomography where global intensity-threshold based methods failed to capture network structure. Based on these cross pipeline comparisons, we demonstrate how organelle and modality specific benchmarking can guide pipeline selection, illustrated by the analysis of metabolic perturbations affecting lipid droplets and mitochondria. Together, these results highlight modality and morphology dependent limitations in common analysis pipelines and provide practical guidance for selecting robust, reproducible strategies for quantitative organelle imaging.