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SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets
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DOI：10.1002/advs.202517738.png)
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Abstract
En 中文
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.
Keywords:
aminoglycoside
hair cell
instance segmentation
machine learning
mitochondria
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