LLAMA: a robust and scalable machine learning pipeline for analysis of cell surface projections in large scale 4D microscopy data
| Autor: | Jennifer L. Stow, James G. Lefevre, Nicholas A. Hamilton, Nicholas D. Condon, Adam A. Wall, Koh Ywh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Macrophage colony-stimulating factor
Scale (ratio) Lipopolysaccharide Computer science business.industry Cell Object (computer science) Machine learning computer.software_genre Pipeline (software) Visualization chemistry.chemical_compound medicine.anatomical_structure chemistry Scalability Microscopy medicine Segmentation Artificial intelligence business computer Filopodia |
| DOI: | 10.1101/2020.12.10.420414 |
| Popis: | We present LLAMA, a pipeline for systematic analysis of terabyte scale 4D microscopy datasets. Analysis of individual biological structures in imaging at this scale requires efficient and robust methods which do not require human micromanagement or editing of outputs. To meet this challenge, we use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, and the generation of detailed object level statistics. Advanced visualisation is a key element of LLAMA: we provide a specialised software tool which supports quality control and optimisation as well as visualisation of outputs. LLAMA was used in a quantitative analysis of macrophage surface membrane projections (filopodia, ruffles, tent-pole ruffles) examining the differential effects of two interventions: lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1). Distinct patterns of increased activity were identified. In addition, a continuity of behaviour was found between tent pole ruffling and wave-like ruffling, further defining the role of filopodia in ruffling. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|