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<p>Cryo-electron tomography (cryo-ET) is a powerful technique for imaging molecular complexes in their native cellular environments. However, identifying the vast majority of molecular species in cellular tomograms remains prohibitively difficult. Machine learning (ML) methods provide an opportunity to automate the annotation process, but algorithm development has been hindered by the lack of large, standardized datasets. Here we present an experimental phantom dataset with comprehensive ground-truth annotations for six molecular species to spur new algorithm development and benchmark existing tools. This annotated dataset is available on the CryoET Data Portal with infrastructure to streamline access for methods developers across fields.</p>

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A realistic phantom dataset for benchmarking cryo-ET data annotation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月26日

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  • Ariana Peck,
  • Yue Yu,
  • Jonathan Schwartz,
  • Anchi Cheng,
  • Utz Heinrich Ermel,
  • Joshua Hutchings,
  • Saugat Kandel,
  • Dari Kimanius,
  • Elizabeth A. Montabana,
  • Daniel Serwas,
  • Hannah Siems,
  • Feng Wang,
  • Zhuowen Zhao,
  • Shawn Zheng,
  • Matthias Haury,
  • David A. Agard,
  • Clinton S. Potter,
  • Bridget Carragher,
  • Kyle Harrington,
  • Mohammadreza Paraan

摘要

Cryo-electron tomography (cryo-ET) is a powerful technique for imaging molecular complexes in their native cellular environments. However, identifying the vast majority of molecular species in cellular tomograms remains prohibitively difficult. Machine learning (ML) methods provide an opportunity to automate the annotation process, but algorithm development has been hindered by the lack of large, standardized datasets. Here we present an experimental phantom dataset with comprehensive ground-truth annotations for six molecular species to spur new algorithm development and benchmark existing tools. This annotated dataset is available on the CryoET Data Portal with infrastructure to streamline access for methods developers across fields.