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Nellie: Automated Organelle Segmentation and Feature Extraction in Live-Cell Microscopy


Core Concepts
Nellie is an automated pipeline for organelle segmentation, tracking, and feature extraction in live-cell microscopy.
Abstract
Introduction of Nellie, an automated pipeline for organelle analysis. Challenges in organelle analysis and the need for unbiased tools like Nellie. Detailed explanation of Nellie's preprocessing steps and feature extraction methods. Case studies showcasing the versatility and effectiveness of Nellie in organelle analysis. Discussion on the potential applications and impact of Nellie in cellular biology research.
Stats
Nellie adapts to image metadata for segmentation. The Frangi filter uses alpha, beta, and gamma parameters for structure enhancement. The Minotri threshold combines Otsu and Triangle methods for segmentation.
Quotes
"We introduce Nellie, an automated pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures." "Nellie adapts to image metadata, eliminating user input for robust hierarchical segmentation."

Key Insights Distilled From

by Austin E. Y.... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13214.pdf
Nellie

Deeper Inquiries

How can tools like Nellie revolutionize the field of cellular biology research

Nellie and similar tools have the potential to revolutionize cellular biology research by providing automated and unbiased pipelines for analyzing organelles in live-cell microscopy. These tools offer a comprehensive solution for segmentation, tracking, and feature extraction of intracellular structures, allowing researchers to delve deep into the dynamic morphology and motility of organelles. By adapting to image metadata and eliminating user input, Nellie streamlines the process of extracting spatial and temporal features at multiple organellar scales. This not only saves time but also ensures consistency in analysis across different datasets. Furthermore, Nellie's ability to generate hierarchical segmentation hierarchies allows for a more detailed understanding of organelle organization within cells. The extracted features can be used for various applications such as unmixing multiple organelle types from single-channel images or training unsupervised graph autoencoders to quantify changes in organellar networks following treatments like Ionomycin exposure. These advanced analyses provide valuable insights into cellular physiology and pathology that were previously challenging or impossible with manual methods. In essence, tools like Nellie empower researchers to conduct more complex analyses on large-scale multidimensional microscopy datasets efficiently and accurately. They open up new possibilities for studying cellular dynamics at a level of detail that was not easily achievable before.

What are the limitations or potential biases that could arise from using automated pipelines like Nellie

While automated pipelines like Nellie offer significant advantages in terms of speed, objectivity, and scalability in analyzing cellular data, there are limitations and potential biases that researchers need to be aware of when using these tools. One limitation is the reliance on predefined algorithms and parameters within the pipeline. If these settings are not optimized correctly or do not account for variations in image quality or biological structures, it could lead to inaccurate segmentations or tracking results. Additionally, automated pipelines may struggle with handling complex multi-scale structures present within datasets if the algorithms are not robust enough. Another potential bias comes from the selection of features extracted by the tool. Depending on how these features are chosen or weighted during analysis (e.g., morphology vs motility features), there is a risk of overlooking important aspects of organelle behavior or structure that could impact downstream interpretations. Moreover, automated pipelines may introduce biases related to dataset preparation or preprocessing steps. For example, variations in imaging conditions (such as laser power) could affect feature extraction outcomes if not properly accounted for during analysis. Researchers must carefully validate results obtained from automated pipelines like Nellie against ground truth data or manual annotations to ensure accuracy and reliability in their findings while being mindful of these limitations.

How can graph-based analyses enhance our understanding of cellular dynamics beyond organelle studies

Graph-based analyses offer a powerful approach to enhance our understanding of cellular dynamics beyond traditional organelle studies by capturing intricate relationships between different components within cells. By representing cell structures as graphs where nodes correspond to specific entities (e.g., skeleton voxels underlying organelles) interconnected based on shared characteristics (features aggregated from surrounding voxels), researchers can analyze complex interactions at various levels simultaneously. This method enables efficient message passing across different distances within an organellar network through multi-level mesh-like graph constructions. The use of graph autoencoders further allows researchers to transform extensive feature outputs into comparable representations via latent space embeddings. These embeddings facilitate geometric comparisons between graphs at different timepoints post-treatment (e.g., Ionomycin exposure), revealing subtle shifts indicative of intrinsic feature oscillations post-treatment response-and-recovery dynamics. Overall, graph-based approaches provide a holistic view of cell organization, allowing scientists to uncover hidden patterns, explore rare events, and gain deeper insights into cellular processes beyond individual organelle behaviors alone
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