Abstract
Recent technical advances in volume electron microscopy (vEM) and artificial-intelligence-assisted image processing have facilitated high-throughput quantifications of cellular structures, such as mitochondria, that are ubiquitous and morphologically diversified. A still often-overlooked computational challenge is to assign a cell identity to numerous mitochondrial instances, for which both mitochondrial and cell membrane contouring used to be required. Here, we present a vEM reconstruction procedure (called mito-SegEM) that utilizes virtual-path-based annotation to assign automatically segmented mitochondrial instances at the cellular scale, therefore bypassing the requirement of membrane contouring. The embedded toolset in webKnossos (an open-source online annotation platform) is optimized for fast annotation, visualization, and proofreading of cellular organelle networks. We demonstrate the broad applications of mito-SegEM on volumetric datasets from various tissues, including the brain, intestine, and testis, to achieve an accurate and efficient reconstruction of mitochondria in a use-dependent fashion.