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<p>MitoGraph is a widely used tool for the automated segmentation of mitochondrial networks in three-dimensional (3D) fluorescence microscopy. However, the emergence of advanced live-cell microscopes such as lattice light-sheet microscopy (LLSM) has produced massive four-dimensional (4D, 3D+time) datasets that highlight a critical bottleneck: current CPU-based implementations are computationally prohibitive, often requiring days or weeks to process. To address this limitation, we developed MitoGraph-GPU, a Python-based GPU implementation that accelerates the dominant filtering steps by vectorizing Hessian/eigenvalue and vesselness computations using CuPy, and streamlines network processing with faster skeletonization and topology analysis. Tested across budding yeast and human lung organoid datasets, MitoGraph-GPU achieves up to 11× speedup in yeast cells and 30× speedup in per-frame segmentation of lung cells. Segmentation fidelity is preserved, with  ~99.9% agreement in maximum intensity projections of segmented images, and minimal differences in downstream measurements. Critically, this throughput enables practical analysis of large 4D datasets : in an LLSM organoid use case (10 movies, 60 frames, ~ 50 cells per movie), total processing time decreases from ~ 500&#xa0;h on CPU to ~ 20&#xa0;h on GPU (25× faster). By producing accurate mitochondrial surfaces and skeletons, MitoGraph-GPU can serve as an efficient segmentation module for downstream mitochondrial tracking and analyses, enabling scalable high-throughput 4D mitochondrial phenotyping.</p>

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GPU-accelerated MitoGraph for high-throughput three-dimensional mitochondrial morphology analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Siddharth Nahar,
  • Zichen Wang,
  • Eric Arkfeld,
  • Gillian McMahon,
  • Hiroyuki Hakozaki,
  • Johannes Schöneberg

摘要

MitoGraph is a widely used tool for the automated segmentation of mitochondrial networks in three-dimensional (3D) fluorescence microscopy. However, the emergence of advanced live-cell microscopes such as lattice light-sheet microscopy (LLSM) has produced massive four-dimensional (4D, 3D+time) datasets that highlight a critical bottleneck: current CPU-based implementations are computationally prohibitive, often requiring days or weeks to process. To address this limitation, we developed MitoGraph-GPU, a Python-based GPU implementation that accelerates the dominant filtering steps by vectorizing Hessian/eigenvalue and vesselness computations using CuPy, and streamlines network processing with faster skeletonization and topology analysis. Tested across budding yeast and human lung organoid datasets, MitoGraph-GPU achieves up to 11× speedup in yeast cells and 30× speedup in per-frame segmentation of lung cells. Segmentation fidelity is preserved, with  ~99.9% agreement in maximum intensity projections of segmented images, and minimal differences in downstream measurements. Critically, this throughput enables practical analysis of large 4D datasets : in an LLSM organoid use case (10 movies, 60 frames, ~ 50 cells per movie), total processing time decreases from ~ 500 h on CPU to ~ 20 h on GPU (25× faster). By producing accurate mitochondrial surfaces and skeletons, MitoGraph-GPU can serve as an efficient segmentation module for downstream mitochondrial tracking and analyses, enabling scalable high-throughput 4D mitochondrial phenotyping.