This project aims to create a comprehensive set of Fiji/ImageJ command-line utilities for nuclear segmentation and fluorescence intensity measurements in Drosophila eye discs (or other complex tissue). It uses Deconvolution Lab for Tikhonov-Mueller deconvolution, WEKA for machine learning-based probability map calculation and 3D ImageJ Suite for final segmentation and 3D object management.
The macros are implemented in Jython and use Java APIs of plugins listed above.
In short, these macros can be used to transform a 3D confocal image like this (only a single slice is shown):
into a list of objects that can be visualized like this:
nuclearP is dual licensed under GPL and CC BY 4.0 with the following restriction on scientific publishing:
This is a scientific piece of software, currently unpublished. You are free to use it in your project, however you should NOT publish your paper before we publish ours, unless you obtain permission from us to publish your paper on callaboration basis.
The above restriction will be removed once our paper is accepted.
To run the segmentation script from console using default options follow this example:
fiji --headless ./src/NuclearSegmentation.py \
--input-dir=./examples/input/ \
--class-model=./examples/nuclei-test.model \
--deconv-psf=./examples/psf-test.tif \
--output-dir=../output
You can set the following segmentation parameters:
fiji --headless ./src/NuclearSegmentation.py \
--input-dir=../Input \
--output-dir=../Output \
--img-channel=1 \
--deconv-psf=../Parameters/PSF-SP8-63x-1024-Venus.tif \
--deconv-lambda=0.01 \
--deconv-k=50 \
--class-model=../Parameters/SP8-63x-1024-Venus.model \
--gauss-xy=0.4 \
--gauss-z=0.4 \
--seg-bkgd-thr=8192 \
--seg-seed-thr=127 \
--seg-seed-r=4 \
--seg-watershed=true \
--seg-vol-min=50 \
--seg-vol-max=10000
To generate a pseudoimage from the raw image and the object list CSV:
fiji --headless ./src/Plotter.py \
--input-csv=../Output/Results/OBJ_mCh-disc-1.csv
--input-image=../Input/mCh-disc-1.tif
--output-image=../Output/Pseudoimage/mCh-disc-1.tif
To generate parameters file for optimizer:
fiji --headless ./src/SegmentationOptimizer.py \
--params-out='parameters.csv' \
--gauss-xy-min=0 \
--gauss-xy-max=5.2 \
--gauss-xy-step=0.2 \
--gauss-z-min=0 \
--gauss-z-steps=5 \
--seg-bkgd-thr-min=4096 \
--seg-bkgd-thr-max=36864 \
--seg-bkgd-thr-step=4096 \
--seg-seed-r-min=2 \
--seg-seed-r-max=13 \
--seg-seed-r-step=2
To run simulation for single paramater set:
fiji --headless ./src/SegmentationOptimizer.py \
--one-shot=true \
--gauss-xy=2 \
--gauss-z=1 \
--seg-bkgd-thr=12288 \
--seg-seed-r=2 \
--input-file=./examples/PM1_C0-sample.tif \
--output-dir=../output/ \
--output-file=simulation-2-1-12288-2.csv
To run simulations for the whole paramater array:
fiji --headless ./src/SegmentationOptimizer.py \
--gauss-xy-min=0 \
--gauss-xy-max=3 \
--gauss-xy-step=1 \
--gauss-z-min=0 \
--gauss-z-steps=1 \
--seg-bkgd-thr-min=12288 \
--seg-bkgd-thr-max=16384 \
--seg-bkgd-thr-step=4096 \
--seg-seed-r-min=2 \
--seg-seed-r-max=4 \
--seg-seed-r-step=2 \
--input-file=./examples/PM1_C0-sample.tif \
--output-dir=../output/ \
--output-file=simulation-results.csv
These are two PBS scripts for use on clusters/supercomputers in the
nuclearP/src/hpc-scripts/ subdirectory. optimizer-worker.pbs is an example
job for optimizing segmentation parameters. fullscale-worker.pbs is an
example of segmentation job. These scripts are using
worker framework and should be
submitted using wsub:
wsub -threaded -batch fullscale-worker.pbs -data image-list.csv