Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

Madugula, Sita S; Massenburg, Lynnicia N; Brown, Spenser R; Bible, Amber N; Harris, Chanda R; Zhang, Lance X; Parker, Kiara; Retterer, Scott T; Morrell-Falvey, Jennifer L; Vasudevan, Rama K; Williams, Alexis N

doi:10.13139/ORNLNCCS/2997581

Title: Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

Dataset
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Abstract

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

Authors:

Madugula, Sita S; Massenburg, Lynnicia N; Brown, Spenser R; Bible, Amber N; Harris, Chanda R; Zhang, Lance X; Parker, Kiara; Retterer, Scott T; Morrell-Falvey, Jennifer L; Vasudevan, Rama K; Williams, Alexis N

Oak Ridge National Laboratory

Publication Date:: Mon Oct 20 04:00:00 UTC 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC)

Subject:: 60 APPLIED LIFE SCIENCES; artificial Intelligence; bacteria; computer vision; machine learning

OSTI Identifier:: 2997581

DOI:: https://doi.org/10.13139/ORNLNCCS/2997581

Citation Formats


                    Madugula, Sita S, Massenburg, Lynnicia N, Brown, Spenser R, Bible, Amber N, Harris, Chanda R, Zhang, Lance X, Parker, Kiara, Retterer, Scott T, Morrell-Falvey, Jennifer L, Vasudevan, Rama K, and Williams, Alexis N. Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures.  United States: N. p., 2025. 
        Web.  doi:10.13139/ORNLNCCS/2997581.

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                    Madugula, Sita S, Massenburg, Lynnicia N, Brown, Spenser R, Bible, Amber N, Harris, Chanda R, Zhang, Lance X, Parker, Kiara, Retterer, Scott T, Morrell-Falvey, Jennifer L, Vasudevan, Rama K, & Williams, Alexis N. Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures.  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2997581

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                    Madugula, Sita S, Massenburg, Lynnicia N, Brown, Spenser R, Bible, Amber N, Harris, Chanda R, Zhang, Lance X, Parker, Kiara, Retterer, Scott T, Morrell-Falvey, Jennifer L, Vasudevan, Rama K, and Williams, Alexis N. 2025.  
"Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2997581.  https://www.osti.gov/servlets/purl/2997581. Pub date:Mon Oct 20 04:00:00 UTC 2025

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@article{osti_2997581,

  title        = {Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures},

  author       = {Madugula, Sita S and Massenburg, Lynnicia N and Brown, Spenser R and Bible, Amber N and Harris, Chanda R and Zhang, Lance X and Parker, Kiara and Retterer, Scott T and Morrell-Falvey, Jennifer L and Vasudevan, Rama K and Williams, Alexis N},

  abstractNote = {This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main},

  doi          = {10.13139/ORNLNCCS/2997581},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Oct 20 04:00:00 UTC 2025},

  month        = {Mon Oct 20 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.13139/ORNLNCCS/2997581

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