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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
Published on: June 16, 2014
napari-lattice: A user-friendly image processing tool for lattice light-sheet microscopy data
Pradeep Rajasekhar1,2, Michael Milton1,2, Niall D Geoghegan1,2
1Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Abstract:
Lattice light-sheet microscopy (LLSM) has revolutionised live-cell imaging with its ability to resolve sub-cellular detail with high spatiotemporal resolution and minimal phototoxicity. However, LLSM often generates large terabyte-scale datasets that require complex preprocessing, creating bottlenecks in storage, data handling and analysis. These challenges are not unique to LLSM but also apply to oblique plane microscopy (OPM). To bridge this gap and reduce the time from large raw data acquisition to biological insight we developed napari-lattice, an open-source tool designed for scalable and modular LLSM workflows. napari-lattice provides a graphical user interface (GUI) for visualisation and analysis, and a command-line interface optimised for High-Performance Computing (HPC) environments. In this article, we demonstrate the capabilities of napari-lattice by quantifying cellular dynamics of human blood neutrophils using an LLSM. Data preprocessing and image analysis were combined into an end-to-end pipeline that used ilastik for cell segmentation and scikit-image for measuring cell morphology and intensity over time. This pipeline was then parallelised over regions of interest (ROIs) on an HPC node using napari-lattice. Restricting the analysis to ROIs reduced the deskewed data size ∼2.6-fold, allowing the full analysis pipeline to complete in ∼12 h, compared to the full field-of-view processing, which did not finish within 48 h. We also demonstrate how napari-lattice can be used with publicly available oblique plane microscopy (OPM) datasets of different configurations. Together, napari-lattice streamlines LLSM data analysis and visualisation, making analysis of complex, feature-rich datasets more accessible. This empowers researchers to fully leverage the potential of LLSM and OPM data.
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