CBI Toolbox is a collection of algorithms used for computational bioimaging and microscopy.

CBI Toolbox is suitable for large scale 3D simulations to reproduce realistic sample sizes and sampling resolution thanks to hardware acceleration (both GPU and multithreading) and optimized implementation of the algorithms. It is flexible enough to emulate different acquisition geometries, and also contains a set of tools used for quantitative evaluation of imaging methods. The framework can also be used for experiment design, as geometries can be virtually tested before being implemented on real acquisition devices.

Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/

Written by François Marelli <francois.marelli@idiap.ch>

For documentation, see https://cbi-toolbox.readthedocs.io

Requirements:

• Python>=3.7
• OpenMP>=2
• MPI (optional, for parallel module)
• CudaToolkit>=8 (optional, for GPU support)

Pip >=10 is highly recommended to ensure the install works.

• Run pip install: pip install cbi-toolbox[mpi,plots,docs,ome]@git+https://github.com/idiap/cbi_toolbox.git (choose optional packages according to your needs)

PyPI hosted version is capped to 1.1.0 due to the addition of non PyPI hosted dependencies

Install from sources:

• Clone the project with its submodules: git clone --recursive <url>
• Run pip install in the root folder: pip install .[mpi,plots,docs,ome] (choose optional packages according to your needs)

The package provides optional dependencies that can be selected at will during the install (in the square brackets):

• mpi: allows to use the cbi_toolbox.parallel.mpi module, requires a functional MPI installation
• plots: installs tools to visualize 3D objects easily
• docs: installs tools used to generate the documentation
• ome: installs tools to read and write ome-tiff images

• Clone the project with its submodules: git clone --recursive <url>
• Create a new environment unsing the environment.yml file: conda env create -f environment.yml -n <environment name>
• Run pip install on the root folder: pip install .[mpi,plots,docs,ome] (choose optional packages according to your needs)

If you already have an MPI implementation installed on your system, it is possible that conda installs a different one. If you want compatibility with your system MPI, uninstall the conda mpi4py and mpi packages, then install mpi4py using pip. It will automatically use your system's MPI version for compilation.

If nvcc is present on the machine, the installation will automatically attempt to compile the software with CUDA support. If you have multiple versions of the CUDA toolkit installed, or if CMake fails to find nvcc automatically, make sure to set the environment variable CUDAToolkit_ROOT to point to the correct tookit folder.

To debug potential installation errors, use pip install . -v to get verbose build logs.

After install, run the following:

import cbi_toolbox.splineradon as spl
spl.is_cuda_available(True)

If the output is other than CUDA support is not installed., the CUDA acceleration was installed successfully.

If you use this package and wish to aknowledge it in an academic publication, please cite the following paper:

Marelli, F. and Liebling, M. "Optics versus computation: influence of illumination and reconstruction model accuracy in Focal-plane-scanning optical projection tomography." 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.

This work was supported by the Swiss National Science Foundation (SNSF), Grants:

• 206021_164022 Platform for Reproducible Acquisition, Processing, and Sharing of Dynamic, Multi-Modal Data. (PLATFORM_MMD)
• 200020_179217 Computational biomicroscopy: advanced image processing methods to quantify live biological systems (COMPBIO)