PyNutil is currently 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/
pip install PyNutil
As input, PyNutil requires:
- An alignment JSON created with the QuickNII or VisuAlign software
- A segmentation file for each brain section with the feature-of-interests displayed in a unique RGB colour (it currently accepts many image formats: png, jpg, jpeg, etc).
Note: The atlases available in PyNutil are those listed via the brainglobe_atlasapi.
PyNutil requires Python 3.8 or above
from PyNutil import PyNutil
"""
Here we define a quantifier object
The segmentations should be images which come out of ilastik, segmenting an object of interest
The alignment json should be out of 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 you wish to use in Quantification.
"""
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.
We are open to feature requests 😊 Simply open an issue in the github describing the feature you would like to see.
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).
Harry Carey, Sharon C Yates, Gergely Csucs, Ingvild Bjerke, Rembrandt Bakker, Nicolaas Groeneboom, Maja A Punchades, Jan G Bjaalie.
GNU General Public License v3
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
Report issues here on Github or email: [email protected]