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

Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Original Publication: [London] : BioMed Central, 2000-

Neural Networks, Computer*; Electron Microscope Tomography/*methods ; Mitochondria/*metabolism ; Mitochondria/*ultrastructure; Algorithms ; Animals ; Automation ; Cell Line ; Cryoelectron Microscopy/methods ; Databases as Topic ; Image Processing, Computer-Assisted ; Models, Theoretical ; Rats ; Signal-To-Noise Ratio

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.

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P41 GM103712 United States GM NIGMS NIH HHS

Keywords: Biomedical image analysis; Cellular structure detection; Cryo-ET; Faster-RCNN

Date Created: 20190331 Date Completed: 20190719 Latest Revision: 20231006

20240105

PMC6439989

10.1186/s12859-019-2650-7

30925860