Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN

Ran Li, Xiangrui Zeng, Stephanie E. Sigmund, Ruogu Lin, Bo Zhou, Chang Liu, Kaiwen Wang, Rui Jiang, Zachary Freyberg, Hairong Lv*, Min Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Background: Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large volume and high content complexity within cells, it remains difficult and time-consuming to localize and identify different components in cellular cryo-ET. To automatically localize and recognize in situ cellular structures of interest captured by cryo-ET, we proposed a simple yet effective automatic image analysis approach based on Faster-RCNN. Results: Our experimental results were validated using in situ cyro-ET-imaged mitochondria data. Our experimental results show that our algorithm can accurately localize and identify important cellular structures on both the 2D tilt images and the reconstructed 2D slices of cryo-ET. When ran on the mitochondria cryo-ET dataset, our algorithm achieved Average Precision >0.95. Moreover, our study demonstrated that our customized pre-processing steps can further improve the robustness of our model performance. Conclusions: In this paper, we proposed an automatic Cryo-ET image analysis algorithm for localization and identification of different structure of interest in cells, which is the first Faster-RCNN based method for localizing an cellular organelle in Cryo-ET images and demonstrated the high accuracy and robustness of detection and classification tasks of intracellular mitochondria. Furthermore, our approach can be easily applied to detection tasks of other cellular structures as well.

Original languageEnglish (US)
Article number132
JournalBMC bioinformatics
Volume20
DOIs
StatePublished - Mar 29 2019

Funding

Publication charge for this work has been funded by the National Key Research and Development Program of China (No. 2018YFC0910404), the National Natural Science Foundation of China (Nos. 61873141, 61721003, 61573207, U1736210, 71871019 and 71471016), and the Tsinghua-Fuzhou Institute for Data Technology. RJ is a RONG professor at the Institute for Data Science, Tsinghua University. This work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. MX acknowledges support of the Samuel and Emma Winters Foundation. ZF acknowledges support from the U.S. Department of Defense (PR141292) and the John F. and Nancy A. Emmerling Fund of The Pittsburgh Foundation. This work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. MX acknowledges support of the Samuel and Emma Winters Foundation. ZF acknowledges support from the U.S. Department of Defense (PR141292) and the John F. and Nancy A. Emmerling Fund of The Pittsburgh Foundation. This work was partially supported by the National Key Research and Development Program of China (No. 2018YFC0910404), the National Natural Science Foundation of China (Nos. 61873141, 61721003, 61573207, U1736210, 71871019 and 71471016), and the Tsinghua-Fuzhou Institute for Data Technology. RJ is a RONG professor at the Institute for Data Science, Tsinghua University.

Keywords

  • Biomedical image analysis
  • Cellular structure detection
  • Cryo-ET
  • Faster-RCNN

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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