napari-tmidas 0.2.6

A plugin for batch processing of confocal and whole-slide microscopy images of biological tissues

napari-tmidas

This napari plugin consists of a growing collection of pipelines for fast batch processing of confocal and whole slide microscopy images of biological tissues. This is a WIP and based on the T-MIDAS terminal.

Features

Currently, napari-tmidas provides pipelines as widgets for batch image conversion and processing, object cropping, label image inspection and ROI colocalization (cf. usage below). You can request new batch image processing features in issues.

Installation

(Video installation guides: https://www.youtube.com/@macromeer/videos)

First, install Napari in a virtual environment:

mamba create -y -n napari-tmidas -c conda-forge python=3.11
mamba activate napari-tmidas
python -m pip install "napari[all]"

Now you can install napari-tmidas via pip:

pip install napari-tmidas

For deep learning features (Batch Crop Anything with SAM2, Spotiflow, Careamics, Trackastra), also install:

pip install 'napari-tmidas[deep-learning]'

Or install everything at once:

pip install 'napari-tmidas[all]'

It is recommended though to install the latest development version. Please also execute this command from time to time in the activated environment to benefit from newly added features:

pip install git+https://github.com/MercaderLabAnatomy/napari-tmidas.git

Additional Setup for Batch Crop Anything

To use the Batch Crop Anything pipeline with SAM2, you only need to install ffmpeg manually. SAM2 and its model checkpoints will be automatically installed when you first run the pipeline:

mamba install -c conda-forge ffmpeg

If you want to batch compress image data using Zstandard, use the package manager of your operating system to install it:

sudo apt-get install zstd # Pre-installed on Linux :man_shrugging:

brew install zstd            # for macOS (requires Homebrew)
pip install zstandard        # Windows with Python >= 3.7

And you are done!

Usage

To use the plugin, start napari in the activated virtual environment with this terminal command:

mamba run -n napari-tmidas napari

You can then find the installed plugin in the Plugins tab.

Microscopy Image Conversion

Converts .lif, .nd2, .czi, .ndpi and Acquifer data to TIF or OME-Zarr formats. Scan a folder, select files, and export with preserved spatial metadata.

Supported Formats:

• TIF - Standard format for compatibility
• OME-Zarr - Recommended for large datasets, spec v0.5 compliant with automatic physical metadata extraction (voxel sizes, spacing)

Image Processing

1. You start with entering the path to the folder containing the images to be processed (currently supports TIF, later also ZARR) and optionally a filter for filename suffix

2. After indexing the files, a table appears with the found images. You can click on them to inspect them in the viewer.

3. Next, select a processing function, set parameters if applicable and Start Batch Processing.

4. You can click on the images in the table to show them in the viewer. For example first click on one of the Original Files, and then the corresponding Processed File to see an overlay.

Note that whenever you click on an Original File or Processed File in the table, it will replace the one that is currently shown in the viewer. So naturally, you'd first select the original image, and then the processed image to correctly see the image pair that you want to inspect.

Processing Function Credits

The image processing capabilities are powered by several excellent open-source tools:

• Cellpose 4: Advanced cell segmentation
• Trackastra: Cell tracking and analysis
• VisCy: Virtual staining using deep learning
• CAREamics: Content-aware image restoration and enhancement
• Spotiflow: Accurate and efficient spot detection for fluorescence microscopy

Processing Function Documentation

Detailed documentation for specific processing functions:

Core Processing

• Basic Processing Functions - Label and intensity operations, channel splitting/merging, time series
• Cellpose Segmentation - Deep learning cell/nucleus segmentation
• TrackAstra Tracking - Cell tracking across time-lapse data
• VisCy Virtual Staining - Virtual staining of phase/DIC images using deep learning

Analysis and Quality Control

• Grid View: Intensity + Labels Overlay - Visual QC for segmentation results
• Intensity-Based Label Filtering - Filter labels by signal intensity
• Regionprops Analysis - Extract quantitative properties from labels

Advanced Processing

• Advanced Processing Functions - Denoising (CAREamics), spot detection (Spotiflow), SciPy/scikit-image filters, compression, colocalization

Batch Label Inspection

If you have already segmented a folder full of images and now you want to maybe inspect and edit each label image, you can use the Plugins > T-MIDAS > Batch Label Inspection, which automatically saves your changes to the existing label image once you click the Save Changes and Continue button (bottom right).

Crop Anything

This pipeline combines the Segment Anything Model (SAM2; supports YX, ZYX and TYX data) for automatic object detection with an interactive interface for selecting and cropping multiple objects from images. To launch the widget, open Plugins > T-MIDAS > Batch Crop Anything. Cropping works like this: Enter 2D view and go to the first z slice where the object to be cropped is appearing. Activate/select the points layer and click on the object. Terminal shows progress. You can then proceed to select another object (always do this in 2D mode)

ROI Colocalization

This pipeline quantifies colocalization between labeled regions of interest (ROIs) across multiple image channels. It determines the extent of overlap between ROIs in a reference channel and those in one or two other channels. The output is a table of colocalization counts. Optionally, the size of reference channel ROIs, as well as the total or median size of colocalizing ROIs in the other channels, can be included. Colocalization is determined using Boolean masking. The number of colocalizing instances is determined by counting unique label IDs within the overlapping regions. Typically, the reference channel contains larger structures, while other channels contain smaller, potentially nested, structures. For example, the reference channel might contain cell bodies, with the second and third channels containing nuclei and sub-nuclear objects, respectively.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-tmidas" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

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This napari plugin was generated with copier using the napari-plugin-template.