SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets

Segmenting individual instances of mitochondria from imaging datasets can provide rich quantitative information, but manual segmentation is prohibitively time-consuming-prompting the development of automated algorithms based on deep neural networks. Existing solutions for various segmentation tasks are optimized for either: high-resolution three-dimensional imaging, relying on well-defined object boundaries (e.g., whole neuron segmentation in volumetric electron microscopy datasets); or low-resolution two-dimensional imaging, boundary-invariant but poorly suited to large 3D objects (e.g., whole-cell segmentation of light microscopy images). However, there is a middle ground that challenges current segmentation tools: large 3D objects with ambiguous boundaries, such as mitochondria in whole-cell 3D electron microscopy datasets. To address this, we developed Skeleton-Oriented Object Segmentation (SKOOTS)-a novel, general-purpose 3D segmentation framework for efficiently segmenting densely packed, morphologically complex objects. SKOOTS is fast, accurate, and memory-efficient, and can be applied to segment mitochondria and other structures in both 3D light and electron microscopy datasets. By combining skeleton-based instance segmentation with a scalable embedding approach, SKOOTS bridges a key gap in existing segmentation strategies and enables biologically meaningful, large-scale analysis of 3D biomedical imaging data. We demonstrate this by segmenting >15 000 mitochondria from cochlear hair cells and supporting cells across experimental conditions in under 2 h on a consumer-grade PC, enabling downstream morphological analysis that revealed subtle structural changes following aminoglycoside exposure. SKOOTS is fully open-source, easy to retrain, and designed to support diverse datasets, making it broadly accessible to the research community.