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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Data and code associated with: napari-lattice: A user-friendly image processing tool for lattice light-sheet microscopy data

Authors: Rajasekhar, Pradeep; Whitehead, Lachlan; Geoghegan, Niall; Rogers, Kelly L.; Ashdown, George; Coussens, Anna; Zaman, Ishrat;

Data and code associated with: napari-lattice: A user-friendly image processing tool for lattice light-sheet microscopy data

Abstract

Neutrophil NETosis — lattice light-sheet imaging & analysis Code and data accompanying the manuscript napari-lattice: A user-friendly image processing tool for lattice light-sheet microscopy data. The pipeline deskews and crops lattice light-sheet (LLS) timelapse data with napari-lattice, segments neutrophil nuclei with an ilastik pixel classifier, and extracts morphological/intensity measurements with scikit-image regionprops. Record contents File / archive Size Description code.zip ~20 MB All code: environment spec, example notebooks, test data, and HPC pipeline scripts. 6h_timelapse-06(1).czi 35.1 GB Zeiss lattice light-sheet timelapse (6 h), SPY650-labelled neutrophil nuclei. 6h_timelapse-06(1)_MIP.czi 302 MB Max-intensity 2D projection of the raw LLS data. 6h_timelapse_1_ROIs.zip ~3 kB Fiji ROIs used to crop individual cells from the timelapse. Supplementary_opm_data.zip 3.7 GB Two oblique-plane-microscopy datasets (raw + deskewed) showing the deskewing pipeline generalises beyond LLS. Data structure record/ ├── README.md overview, install, and usage (start here) ├── code.zip │ └── code/ │ ├── README.md copy of this overview │ ├── environment.yml conda/mamba environment spec │ ├── data/ empty; copy the raw .czi / ROIs here for the HPC pipeline │ ├── examples/ runnable demo (no cluster / raw data needed) │ │ ├── test_entire_workflow.ipynb end-to-end segmentation + measurement demo │ │ ├── create_napari_workflow.ipynb how the napari-workflows .yml is built │ │ └── test_data/ three small 3D crops (+ mask) and their README │ └── hpc_scripts/ SLURM batch pipeline (run on a cluster) │ ├── workflow/ segmentation function, ilastik classifier, workflow .yml │ ├── crop_analysis/ deskew + crop + run the segmentation workflow │ ├── crop_deskew_only/ deskew + crop only │ ├── deskew_only/ deskew only │ └── benchmarking/ runtime/scaling benchmarks (ROI vs full-FOV) ├── 6h_timelapse-06(1).czi raw LLS timelapse ├── 6h_timelapse-06(1)_MIP.czi max-intensity projection ├── 6h_timelapse_1_ROIs.zip Fiji cropping ROIs └── Supplementary_opm_data.zip ├── brain_organoid/ 4×-expanded brain organoid, direct-view OPM (raw + deskewed + config) └── thy1_eGFP/ Thy1-GFP mouse brain, scanned oblique plane (SOPi) (raw + deskewed + config) Start with README.md. Software: Python 3.10, ilastik-core 1.4.1, lls_core 1.2.1 (see environment.yml).

Keywords

oblique plane microscopy, lattice lightsheet, Biomedical Research, Microscopy, Fluorescence, Image processing, Cell Death

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average