PyNutil 0.5.1

a package to quantify atlas registered brain data

PyNutil

PyNutil is still under development and the API is subject to change

PyNutil is a Python library for brain-wide quantification and spatial analysis of features in serial section images from the brain. It aims to replicate the Quantifier feature of the Nutil software (RRID: SCR_017183).

PyNutil is able to integrate outputs from atlas registration software and image segmentation software in order to produce atlas based quantifications, 3D point clouds, and 3D heatmaps of brain derived data.

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

Python package

pip install PyNutil 

Running demos

The scripts in demos/ assume PyNutil is importable as an installed package. For development, install in editable mode from the repository root:

pip install -e .

GUI

download the executable for Windows and macOS via the GitHub releases tab

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/non_linear_allen_mouse/segmentations/',
    alignment_json='../tests/test_data/non_linear_allen_mouse/alignment.json',
    colour=[0, 0, 0],
    atlas_name='allen_mouse_25um'
    #If you would like to use cellpose segmentations add:
    #segmentation_format = 'cellpose'
)

#optionally, if you want to generate a 3D heatmap
pnt.interpolate_volume()

pnt.get_coordinates(object_cutoff=0)

pnt.quantify_coordinates()

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

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

Per-Hemisphere Quantification

If you use an atlas which has a hemisphere map (All brainglobe atlases have this, it is a volume in the shape of the atlas with 1 in the left hemisphere and 2 in the right) PyNutil will generate per-hemisphere quantifications in addition to total numbers. In addition, PyNutil will also genearte additional per-hemisphere point cloud files for viewing in meshview.

Damage Quantification

The QCAlign tool allows you to mark damaged areas on your section. This means that these damaged areas are excluded from your point clouds. In addition, PyNutil will separately quantify damaged and undamaged areas. Note the undamaged and damaged column names.

Meshview json files

PyNutil will produce meshview json files. These can be opened in MeshView for the Allen Mouse or for the Waxholm Rat

https://github.com/user-attachments/assets/d3a43ca9-133e-40d1-a1b9-9a359deabf2d

Siibra explorer compatible NifTI files

If you have interpolated your volume you will find an interpolated NifTI volume in your output directory. This can be dragged and dropped directly into siibra explorer. If you share your data you can also include a shareable link to your data in the Siibra viewer. These files are also viewable in ITK-SNAP.

https://github.com/user-attachments/assets/30ca7f7f-92f5-4d83-a92a-b29603181b8f

Interpreting the Results

Each column name has the following definition

Column	Definition
idx	The atlas ID of the region.
name	The name of atlas region.
r	The amount of red in the RGB value for the region colour.
g	The amount of green in the RGB value for the region colour.
b	The amount of blue in the RGB value for the region colour.
Region area	Area representing the region on the segmentation in pixel values.
Object count	Number of objects located in the region. An object is a disconnected group of pixels
Object pixels	Number of pixels representing objects in this region.
Object area	Area representing objects in this region (object pixels x pixel scale).
Area fraction	Ratio of Object pixels to Region pixels (Object pixels / Region pixels).
Left hemi	For each of the other columns, what is that value for the left hemisphere alone
Right hemi	For each of the other columns, what is that value for the right hemisphere alone
Damaged	For each of the other columns, what is that value for the areas marked damaged alone
Undamaged	For each of the other columns, what is that value for the areas marked undamaged alone

If you choose to measure the intensity of images rather than segmentations you will not get object counts. Instead you will get

Column	Definition
Sum intensity	The sum of all image pixels in a region.
Mean inensity	The mean of all image pixels in a region.

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]