.. list-table:: :widths: 25 25 :header-rows: 1
.. |GitHub Badge| image:: https://img.shields.io/badge/github-repo-000.svg?logo=github&labelColor=gray&color=blue :target: https://github.com/RiosGroup/stapl3d :alt: GitHub Badge
.. |License Badge| image:: https://img.shields.io/github/license/RiosGroup/stapl3d :target: https://github.com/RiosGroup/STAPL3D :alt: License Badge
STAPL-3D (SegmenTation Analysis by ParaLlelization of 3D Datasets) is an analysis tool for mLSR-3D or other high dimensional 3D fluorescency microscopy datasets.
In brief, the STAPL-3D pipeline is characterized by:
What STAPL-3D does to allow accurate analysis can be summarized as:
.. A STAPL-3D legacy repository with potentially useful additional code can be found here: https://github.com/michielkleinnijenhuis/segmentation
Hardware requirements
STAPL-3D is created for deployment on High Performance Computing clusters and tested with SGE and SLURM job schedulers. STAPL-3D can also be run on (high-end) workstations. Our lab uses HP Z8 G4 with dual Intel Xeon Gold 5122 3.6 GHz processors, 1 TB RAM, 3x2TB SSD + 6x5TB HDD in RAID-5, and an Nvidia Quadro P4000 8GB graphics card. This workstation setup can process a 100 x 15000 x 15000 x 8 voxel dataset within a day. More modest systems are also feasible: the RAM requirements scale with the chosen parallelization blocksize and is approximately 40GB for a blocksize of 100x1280x1280 voxels for the membrane enhancement step. We would recommend a minimum of 16GB of RAM.
Software requirements
This software runs on Linux, MacOS and Windows. It has been tested on:
dependencies: Python (auto-installed through with conda or pip)
python>=3.8
scikit-image
h5py
matplotlib
numpy
pyyaml
scipy
pandas
pypdf2
czifile
nibabel
simpleitk
Other software packages:
ACME <https://wiki.med.harvard.edu/SysBio/Megason/ACME>
_Ilastik <https://www.ilastik.org/documentation/basics/installation.html>
_ (optional)Fiji <https://imagej.net/downloads>
_ (optional)STAPL-3D is most easily installed (< 5 min) with conda <https://docs.conda.io/en/latest>
_ by pasting the following code in the terminal:
.. code-block:: console
git clone https://github.com/RiosGroup/STAPL3D.git cd STAPL3D
conda env create -f environment.yml conda activate stapl3d pip install .
To use membrane enhancement in the pipeline, ACME <https://wiki.med.harvard.edu/SysBio/Megason/ACME>
needs to be installed. To use the machine-learning channel clean-up procedures, Ilastik <https://www.ilastik.org/documentation/basics/installation.html>
needs to be installed. To run the demos, these two external packages are not required as we provide these steps.
Demos
Jupyter notebooks <https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/index.html>
are provided in the demos directory to showcase the basic functionality of STAPL-3D. The data for the demo (~6GB) can be downloaded here <https://surfdrive.surf.nl/files/index.php/s/Q9wRT5cyKGERxI5>
.
If you installed STAPL-3D using conda, you can start a notebook in the stapl3d enviroment as follows:
.. code-block:: console
conda activate stapl3d python -m ipykernel install --user --name=stapl3d jupyter notebook
HPC deployment
Basic instruction for running STAPL3D on your own data
If you want to contribute to the development of STAPL3D,
have a look at the contribution guidelines <CONTRIBUTING.rst>
_.
Copyright (c) 2020,
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
This package was created with Cookiecutter <https://github.com/audreyr/cookiecutter>
and the NLeSC/python-template <https://github.com/NLeSC/python-template>
.