A library for classifying and tracking ROIs.
Project description
Welcome to ROICaT
- Documentation: https://roicat.readthedocs.io/en/latest/
- Discussion forum: https://groups.google.com/g/roicat_support
- Technical support: Github Issues
Region Of Interest Classification and Tracking ᗢ
A simple-to-use Python package for automatically classifying images of cells and tracking them across imaging sessions/planes.
Why use ROICaT?
- ROICaT was made to be better than existing tools. It is capable of classifying and tracking neuron ROIs at accuracies approaching human performance. Several labs currently use ROICaT to do automatic tracking and classification of ROIs with no post-hoc curation required.
- Great effort was taken to optimize performance. Computational requirements are minimal and run times are fast.
- It's easy to use. You don't need to know how to code. You can use the interactive notebooks to run the pipelines with just a few clicks.
With ROICaT, you can:
- Classify ROIs into different categories (e.g. neurons, dendrites, glia, etc.).
- Track ROIs across imaging sessions/planes (e.g. ROI #1 in session 1 is the same as ROI #7 in session 2).
What data types can ROICaT process?
- ROICaT can accept any imaging data format including: Suite2p, CaImAn, CNMF, NWB, raw/custom ROI data and more. See below for details on how to use any data type with ROICaT.
What are the minimum computing needs?
- We recommend the following as a starting point:
- 4 GB of RAM (more for large data sets e.g., ~32 GB for 100K neurons)
- GPU not required but will increase run speeds ~5-50x
How to use ROICaT
Listed below, we have a suite of easy to run notebooks for running the ROICaT pipelines.
- The Google CoLab notebooks can be run fully remotely without installing anything on your computer.
- The Jupyter notebooks can be run locally on your computer and require you to install ROICaT.
TRACKING:
CLASSIFICATION:
- Interactive notebook - Drawing
- Google CoLab - Drawing
- Interactive notebook - Labeling
- Interactive notebook - Train classifier
- Interactive notebook - Inference with classifier
OTHER:
- Custom data importing notebook
- Use the API to integrate ROICaT functions into your own code: Documentation.
General workflow:
- Pass ROIs through ROInet: Images of the ROIs are passed through a neural network which outputs a feature vector for each image describing what the ROI looks like.
- Classification: The feature vectors can then be used to classify ROIs:
- A simple regression-like classifier can be trained using user-supplied labeled data (e.g. an array of images of ROIs and a corresponding array of labels for each ROI).
- Alternatively, classification can be done by projecting the feature vectors into a lower-dimensional space using UMAP and then simply circling the region of space to classify the ROIs.
- Tracking: The feature vectors can be combined with information about the position of the ROIs to track the ROIs across imaging sessions/planes.
Installation
ROICaT works on Windows, MacOS, and Linux. If you have any issues during the installation process, please make a github issue with the error.
0. Requirements
- Your segmented data. For example Suite2p output data (stat.npy and ops.npy files), CaImAn output data (results.h5 files), or any other type of data using this custom data importing notebook.
- Anaconda or Miniconda.
- If using Windows: Microsoft C++ Build Tools
- If using linux/unix(MacOS): GCC >= 5.4.0, ideally == 9.2.0. Check with:
gcc --version
, or just google how to do this on your operating system. - Optional: CUDA compatible NVIDIA GPU and drivers. Using a GPU can increase ROICaT speeds ~5-50x, though without it, ROICaT will still run reasonably quick. GPU support is not available for Macs.
- The below commands should be run in the terminal (Mac/Linux) or Anaconda Prompt (Windows).
1. (Recommended) Create a new conda environment
conda create -n roicat python=3.11
conda activate roicat
pip install --upgrade pip
You will need to activate the environment with conda activate roicat
each time
you want to use ROICaT.
2. Install ROICaT
pip install --user roicat[all]
pip install git+https://github.com/RichieHakim/roiextractors
Note: if you are using a zsh terminal, change command to: pip3 install --user 'roicat[all]'
For installing GPU support on Windows, see
Troubleshooting below.
3. Clone the repo to get the scripts and notebooks
git clone https://github.com/RichieHakim/ROICaT
Then, navigate to the ROICaT/notebooks/jupyter
directory to run the notebooks.
Troubleshooting Installation
Troubleshooting (Windows)
If you receive the error: ERROR: Could not build wheels for hdbscan, which is required to install pyproject.toml-based projects
on Windows, make sure that
you have installed Microsoft C++ Build Tools. If not, download from
here and run the
commands:
cd path/to/vs_buildtools.exe
vs_buildtools.exe --norestart --passive --downloadThenInstall --includeRecommended --add Microsoft.VisualStudio.Workload.NativeDesktop --add Microsoft.VisualStudio.Workload.VCTools --add Microsoft.VisualStudio.Workload.MSBuildTools
Then, try proceeding with the installation by rerunning the pip install commands above. (reference)
Troubleshooting (GPU support)
GPU support is not required. Windows users will often need to manually install a
CUDA version of pytorch (see below). Note that you can check your nvidia driver
version using the shell command: nvidia-smi
if you have drivers installed.
Use the following command to check your PyTorch version and if it is GPU enabled:
python -c "import torch, torchvision; print(f'Using versions: torch=={torch.__version__}, torchvision=={torchvision.__version__}'); print(f'torch.cuda.is_available() = {torch.cuda.is_available()}')"
Outcome 1: Output expected if GPU is enabled:
Using versions: torch==X.X.X+cuXXX, torchvision==X.X.X+cuXXX
torch.cuda.is_available() = True
This is the ideal outcome. You are using a CUDA version of PyTorch and your GPU is enabled.
Outcome 2: Output expected if non-CUDA version of PyTorch is installed:
Using versions: torch==X.X.X, torchvision==X.X.X
OR
Using versions: torch==X.X.X+cpu, torchvision==X.X.X+cpu
torch.cuda.is_available() = False
If a non-CUDA version of PyTorch is installed, please follow the
instructions here: https://pytorch.org/get-started/locally/ to install a CUDA
version. If you are using a GPU, make sure you have a CUDA compatible NVIDIA
GPU and
drivers that match the same
version as the PyTorch CUDA version you choose. All CUDA 11.x versions are
intercompatible, so if you have CUDA 11.8 drivers, you can install
torch==2.0.1+cu117
.
Solution 2:
If you are sure you have a compatible GPU and correct drivers, you can force install the GPU version of pytorch, see the pytorch installation instructions. Links for the latest version or older versions. Example:
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
Outcome 3: Output expected if CUDA version of PyTorch is installed but GPU is not available:
Using versions: torch==X.X.X+cuXXX, torchvision==X.X.X+cuXXX
torch.cuda.is_available() = False
If a CUDA version of PyTorch is installed but GPU is not available, make sure
you have a CUDA compatible NVIDIA GPU
and drivers that match the
same version as the PyTorch CUDA version you choose. All CUDA 11.x versions are
intercompatible, so if you have CUDA 11.8 drivers, you can install
torch==2.0.1+cu117
.
TODO:
- Some more integration tests
- Switch to ONNX for ROINet
- Add more documentation / tutorials
- Make a GUI
- Finish ROIextractors integration
- Make a Docker container
- Make a standard classifier
- Write the paper
- Make tweet about it
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