Abstract
Abstract
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.