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Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-dimensional (3D) arrangement requires volumetric analysis of serial electron microscopy and has therefore been limiting for unbiased quantitative assessment. Here, we developed a novel, publicly available, deep learning (DL)-based image analysis platform called\n                    <jats:bold>P<\/jats:bold>\n                    ython-based\n                    <jats:bold>h<\/jats:bold>\n                    uman-\n                    <jats:bold>i<\/jats:bold>\n                    n-the-\n                    <jats:bold>lo<\/jats:bold>\n                    op\n                    <jats:bold>w<\/jats:bold>\n                    orkflow (PHILOW) implemented with a human-in-the-loop (HITL) algorithm. 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