PyNutil 0.2.21

a package to translate data between common coordinate templates

PyNutil

PyNutil is under development.

PyNutil is a Python library for brain-wide quantification and spatial analysis of features in serial section images from mouse and rat brain. It aims to replicate the Quantifier feature of the Nutil software (RRID: SCR_017183). It builds on registration to a standardised reference atlas with the QuickNII (RRID:SCR_016854) and VisuAlign software (RRID:SCR_017978) and feature extraction by segmentation with an image analysis software such as ilastik (RRID:SCR_015246).

For more information about the QUINT workflow: https://quint-workflow.readthedocs.io/en/latest/

Available Atlases

PyNutil can be run using a custom atlas in .nrrd format (e.g. tests/test_data/Allen_mouse_2017_atlas)

Pynutil can also be used with the atlases available in the BrainGlobe_Atlas API.

Installation

pip install PyNutil

Usage

PyNutil requires Python 3.8 or above.

As input, PyNutil requires:

1. An atlas
2. A corresponding alignment JSON created with the QuickNII or VisuAlign software.
3. A segmentation file for each brain section with the features to be quantified displayed with a unique RGB colour code (it currently accepts many image formats: png, jpg, jpeg, etc).

from PyNutil import PyNutil

"""
Here we define a quantifier object
The segmentations should be images which come out of ilastik, segmenting objects-of-interest
The alignment json should be from DeepSlice, QuickNII, or VisuAlign, it defines the sections position in an atlas
The colour says which colour is the object you want to quantify in your segmentation. It is defined in RGB
Finally the atlas name is the relevant atlas from brainglobe_atlasapi or a custom atlas in nrrd format.

basic_example.py (brainglobe_atlasapi)
basic_example_custom_atlas.py (custom atlas)

"""
pnt = PyNutil(
    segmentation_folder='../tests/test_data/big_caudoputamen_test/',
    alignment_json='../tests/test_data/big_caudoputamen.json',
    colour=[0, 0, 0],
    atlas_name='allen_mouse_25um'
)

pnt.get_coordinates(object_cutoff=0)

pnt.quantify_coordinates()

pnt.save_analysis("PyNutil/outputs/myResults")

PyNutil generates a series of reports in the folder which you specify.

Feature Requests

We are open to feature requests 😊 Simply open an issue in the github describing the feature you would like to see.

Acknowledgements

PyNutil is developed at the Neural Systems Laboratory at the Institute of Basic Medical Sciences, University of Oslo, Norway with support from the EBRAINS infrastructure, and funding support from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Framework Partnership Agreement No. 650003 (HBP FPA).

Contributors

Harry Carey, Sharon C Yates, Gergely Csucs, Arda Balkir, Ingvild Bjerke, Rembrandt Bakker, Nicolaas Groeneboom, Maja A Puchades, Jan G Bjaalie.

Licence

GNU General Public License v3

Related articles

Yates SC, Groeneboom NE, Coello C, et al. & Bjaalie JG (2019) QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain. Front. Neuroinform. 13:75. https://doi.org/10.3389/fninf.2019.00075

Groeneboom NE, Yates SC, Puchades MA and Bjaalie JG. Nutil: A Pre- and Post-processing Toolbox for Histological Rodent Brain Section Images. Front. Neuroinform. 2020,14:37. https://doi.org/10.3389/fninf.2020.00037

Puchades MA, Csucs G, Lederberger D, Leergaard TB and Bjaalie JG. Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool. PLosONE, 2019, 14(5): e0216796. https://doi.org/10.1371/journal.pone.0216796

Carey H, Pegios M, Martin L, Saleeba C, Turner A, Everett N, Puchades M, Bjaalie J, McMullan S. DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas. BioRxiv. https://doi.org/10.1101/2022.04.28.489953

Berg S., Kutra D., Kroeger T., Straehle C.N., Kausler B.X., Haubold C., et al. (2019) ilastik:interactive machine learning for (bio) image analysis. Nat Methods. 16, 1226–1232. https://doi.org/10.1038/s41592-019-0582-9

Contact us

Report issues here on Github or email: [e-mail]