PyPI 官网下载 | vollseg-1.9.1.tar.gz

# VollSeg 3D segmentation tool for irregular shaped cells  [](https://travis-ci.com/github/kapoorlab/vollseg) [](https://pypi.org/project/vollseg/) ## Installation This package can be installed by `pip install --user vollseg` If you are building this from the source, clone the repository and install via ```bash git clone https://github.com/kapoorlab/vollseg/ cd vollseg pip install --user -e . # or, to install in editable mode AND grab all of the developer tools # (this is required if you want to contribute code back to NapaTrackMater) pip install --user -r requirements.txt ``` ### Pipenv install Pipenv allows you to install dependencies in a virtual environment. ```bash # install pipenv if you don't already have it installed pip install --user pipenv # clone the repository and sync the dependencies git clone https://github.com/kapoorlab/vollseg/ cd vollseg pipenv sync # make the current package available pipenv run python setup.py develop # you can run the example notebooks by starting the jupyter notebook inside the virtual env pipenv run jupyter notebook ``` Access the `example` folder and run the cells. ## Algorithm  Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view. ## Example To try the provided notebooks we provide an example dataset of MDA231 human breast carcinoma cells infected with a pMSCV vector including the GFP sequence, embedded in a collagen matrix from Dr. R. Kamm. Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA (USA)[tracking challenge](http://celltrackingchallenge.net/3d-datasets/), download the hyperstacks of the Raw, instance and semantic segmentation masks from [here](https://drive.google.com/drive/folders/1ze8KsrFI0-UTrsMnAPomiyf4sN8aCm__?usp=sharing). Pretrained model weights for denoising done via noise to void, segmentation done via U-Net and Stardist are also in the directory. For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb) ## Docker A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with: ~~~bash docker build -t voll . ~~~ Now to run the `track` command: ~~~bash # show help docker run --rm -it voll ~~~ ## Requirements - Python 3.7 and above. ## License Under MIT license. See [LICENSE](LICENSE). ## Authors - Varun Kapoor <randomaccessiblekapoor@gmail.com> - Claudia Carabaña - Mari Tolonen