Extends QuPath's functionalities for image analysis of serial brain sections across many animals. It is designed for multichannel cell segmentation across large and variable datasets and ensures consistency in image analysis across large datasets. This module leverages QuPath's built-in algorithms to provide a multi-channel, whole-brain optimised object detection pipeline. BraiAnDetect features options for refining signal quantification, including machine learning-based object classification, region specific cell segmentation, multiple marker co-expression algorithms and an interface for selective exclusion of damaged tissue portions. It works best if coupled with:
qupath-extension-abba: for importing brain atlas annotations from ABBAbraian: the associated python library for whole-brain analysis and visualization
YSK: BraiAn's names stands for Brain Analysis.
I suggest you to listen to "Brianstorm" by Arctic Monkey while working with BraiAn ;)
This extension helps you manage image analysis across multiple QuPath projects ensuring consistency. In particular, it is designed to perform batch analysis across many QuPath projects in an automated manner. Typically, in whole-brain datasets, one brain = one QuPath project and BraiAn makes sure the exact same analysis parameters are consistently applied across different projects. It was first developed to work with ABBA, but can be used for other purposes as well. Its core idea is to move the input image analysis parameters used to analyse multiple QuPath projects of the same cohort/experiment outside of scripts' code (in a YAML configuration file, see below). This allows having a reproducible configuration that can be shared, backed up and ran after long periods of time.
The extensions exposes a proper library API. Here are some examples. It allows you to:
- multi-channel automatic object segmentation (e.g. cell detections)
- machine-learning-based object classification (e.g. apply custom classifiers on different detection types).
- co-localization analysis (i.e. quickly find all detections that are double—or triple/multiple—positive, through
BoundingBoxHierarchy) - fine tune image analysis using channel histograms
- tag certain brain regions to be excluded from further analysis due to tissue, imaging or alignment problems
- export to file the quantification results (number of detections/double+ found in each brain region)
- export to file a list of regions flagged to be excluded
Where to start from, though? Reading this script and the associated config file is a good start!
If you use BraiAn in your work, please cite the paper below, currently in pre-print:
Important
Chiaruttini, N., Castoldi, C. et al. ABBA, a novel tool for whole-brain mapping, reveals brain-wide differences in immediate early genes induction following learning. bioRxiv (2024). https://doi.org/10.1101/2024.09.06.611625
We decided to publish the BraiAn pipeline (i.e. this QuPath extension and the twin python libray) with the most libre licence possible because we find maximum value in learning from others and sharing our own—small—knowledge.
We, developers in neuroscience, are islands that often work alone and frequently end up reinventing the wheel rather then spending time finding, pickup up and adapting the work that somebody, from the other side of the world, did with no intention to publish. For this reason we spent a great amount of our personal time making the modules as usable, extensible, and long-lasting as we could. And yet, we know it could be better, that there could be bugs, unforeseen scenarios and missing features.
For this reason we hope that, if you find our work useful, you will find time and will to contribute back upstream with issues, PRs, documentation, tests, feature requests... Any activity makes us happy!
You can build the QuPath BraiAn extension from source with:
./gradlew clean buildThe built .jar extension file will be under build/libs.