Efficient cell-wide mapping of mitochondria in electron microscopic volumes using webKnossos.

Yi Jiang, Haoyu Wang, Kevin M Boergens, Norman Rzepka, Fangfang Wang, Yunfeng Hua
Author Information
  1. Yi Jiang: Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 200125 Shanghai, China. Electronic address: yi.jiang@shsmu.edu.cn.
  2. Haoyu Wang: Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 200125 Shanghai, China.
  3. Kevin M Boergens: Department of Physics, University of Illinois Chicago, Chicago, IL 60607, USA.
  4. Norman Rzepka: Scalable Minds GmbH, 14482 Potsdam, Germany.
  5. Fangfang Wang: Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 200125 Shanghai, China.
  6. Yunfeng Hua: Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 200125 Shanghai, China. Electronic address: yunfeng.hua@shsmu.edu.cn.

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.

Keywords

MeSH Term

Mitochondria
Humans
Animals
Microscopy, Electron
Image Processing, Computer-Assisted
Software
Male
Mice
Brain

Word Cloud

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