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B6_dapi_template
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10.12751/g-node.16wrxa
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README.md

Description

This repository contains the full data set for constructing the DAPI template, the full DAPI template creation pipeline and the automatic slice segmentation.

Automatic slice segmentation

Program for automatic segmentation to a DAPI-stained coronal mouse brain slices.

Dependencies

Prerequisites

  • A high-resolution, DAPI modality TIFF image of a brain slice. Can have multiple channels.
  • A template file (.nii or .nii.gz)
  • A segmentation of the template file (.nii or .nii.gz)

Usage

  • Use preprocess.py to prepare input slice for automatic registration
  • Use auto_seg.py with preprocessed slice which will output a folder containing segmentation registered to slice.

Details

Detailed parameter description.

auto_seg.py

Usage

python3 auto_seg.py sliceloc segloc templateloc --bregma [coord]

Positional/Required Arguments
sliceloc Location of the slice you wish to register (.nii or.nii.gz)
segloc Location of file containing segmentation of the template (.nii or.nii.gz)
templateloc Location of template file (.nii or .nii.gz)
--bregma [coord] Bregma coordinate of input slice (if --approx argument is present, this is an optional argument)

Optional arguments
--approx [index] Provide the location of the slice as a z-index of the template. Can be used instead of --bregma argument.
--out [directory] Provide an alternative output directory (default directory is /output)
--dapi [index] Index of the DAPI channel in the slice, by default the DAPI channel is assumed to be the last channel

preprocess.py

Usage

python3 preprocess.py file dir -s Series --pdim [pixeldimensions]

Positional/Required Arguments
file Location of the file to be preprocessed (.tiff or .nd2)
dir Directory to output preprocessed files (will output .nii files)
-s The series to extract (If input file is single-series, use 0 for this argument)

Optional arguments
--pdim [pixeldimensions] Dimensions of pixels, if not provided, these will be extracted from image metadata
datacite.yml
Title A three-dimensional, population-based average of the C57BL/6 mouse brain from DAPI-stained coronal slices
Authors Stæger,Frederik Filip;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;ORCID:0000-0002-2295-8637
Mortensen,Kristian;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;ORCID:0000-0003-3726-0781
Kaufmann,Louis;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
Hirase,Hajime;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;ORCID:0000-0003-3806-6905
Sigurdsson,Björn;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;ORCID:0000-0002-7484-7779
Nedergaard,Maiken;Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark and Center for Translational Neuromedicine, University of Rochester Medical Center, Rochester, NY 14642, USA.;ORCID:0000-0001-6502-6031
Description The full data set and pipeline for constructing the three-dimensional, population-based average of the C57BL/6 mouse brain form DAPI-stained coronal slices. This repository also contains a python implementation of automatic coronal brain slice segmentation. The data set constitutes of all the raw slice images (.tif) in full resolution, the pre-processed version (.nii), the individually reconstructed brain volumes, and the final population-based average.
License Creative Commons CC0 1.0 Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/)
References A three-dimensional, population-based average of the C57BL/6 mouse brain from DAPI-stained coronal slices [] (IsDescribedBy)
Funding
Keywords Neuroscience
Mouse brain template
C57BL/6 brain template
DAPI
Population-based average
Automatic segementation
Resource Type Dataset