Summary of Nellie: Automated Organelle Segmentation, Tracking, and Hierarchical Feature Extraction in 2d/3d Live-cell Microscopy, by Austin E. Y. T. Lefebvre (1) et al.
Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy
by Austin E. Y. T. Lefebvre, Gabriel Sturm, Ting-Yu Lin, Emily Stoops, Magdalena Preciado Lopez, Benjamin Kaufmann-Malaga, Kayley Hake
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents Nellie, a novel pipeline for analyzing dynamic organelles. This automated and unbiased tool eliminates the need for user input, allowing for efficient segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie’s preprocessing pipeline enhances structural contrast on multiple scales, enabling robust hierarchical segmentation of sub-organellar regions. The paper also introduces internal motion capture markers and a radius-adaptive pattern matching scheme to track sub-voxel flow. Nellie extracts a plethora of features at multiple levels for deep analysis, making it suitable for various applications such as organelle unmixing, graph autoencoder training, and endoplasmic reticulum network characterization. The tool features a point-and-click GUI and open-source codebase, making it accessible to both novice and experienced users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to analyze tiny structures inside cells without needing to manually adjust settings or understand complicated technical details! A team of researchers has developed a new tool called Nellie that can do just that. Nellie is designed to help scientists study how these tiny structures, like mitochondria and endoplasmic reticulum, move and change over time. The tool can automatically segment and track these structures, extract important features, and even allow users to visualize the results without needing to write code. This breakthrough could lead to new discoveries in biology and medicine. |
Keywords
* Artificial intelligence * Autoencoder * Feature extraction * Pattern matching * Tracking
