ArticleBMC bioinformatics2019
Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.Analytical chemistry · 2026Article
- Preparation of Vitrified Mammalian Cells for In Situ Cryo-Electron Tomography.Current protocols · 2025Article
- A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.Scientific reports · 2025Article
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- Few-shot classification of Cryo-ET subvolumes with deep Brownian distance covariance.Briefings in bioinformatics · 2024Article
- Semi-automated, high-content imaging of drug transporter knockout sea urchin (Lytechinus pictus) embryos.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2024Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.Animals : an open access journal from MDPI · 2023Article
- Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.Bio-design and manufacturing · 2023Review
- Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- High-Resolution Computer Tomography Image Features of Lungs for Patients with Type 2 Diabetes under the Faster-Region Recurrent Convolutional Neural Network Algorithm.Computational and mathematical methods in medicine · 2022Article
- Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.PLoS computational biology · 2020Article
- Spark-based parallel calculation of 3D fourier shell correlation for macromolecule structure local resolution estimation.BMC bioinformatics · 2020Article
- A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.BMC bioinformatics · 2020Article
- Deep learning in fracture detection: a narrative review.Acta orthopaedica · 2020Review
- One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2020Article
- Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization.BMC bioinformatics · 2019Article
- Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms.Bioinformatics (Oxford, England) · 2019Article
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11 authors.
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Abstract
backgroundCryo-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.
resultsOur 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.
conclusionsIn 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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