# rivuletpy

**Repository Path**: junma11/rivuletpy

## Basic Information

- **Project Name**: rivuletpy
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-24
- **Last Updated**: 2020-12-19

## Categories & Tags

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**Tags**: None

## README

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# Rivuletpy
## Example Neuron Tracings
![alt text](meta/rivulet2_showcase.png "neuron showcase")
## Example Lung Airway Tracing
![alt text](meta/rivulet2_airway.png "airway showcase")

## Rivuletpy == Rivulet2 
Rivuletpy is a Python3 toolkit for automatically reconstructing single neuron models from 3D microscopic image stacks & other tree structures from 3D medical images.

It is actively maintained and being used in industry scale image analysis applications. 

The project was initiated in the [BigNeuron project](https://alleninstitute.org/bigneuron/about/)

The `rtrace` command is powered by the Rivulet2 algorithm published in IEEE Trans. TMI:

[1] S. Liu, D. Zhang, Y. Song, H. Peng and W. Cai, "Automated 3D Neuron Tracing with Precise Branch Erasing and Confidence Controlled Back-Tracking," in IEEE Transactions on Medical Imaging. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8354803&isnumber=4359023

PDF [https://www.biorxiv.org/content/biorxiv/early/2017/11/27/109892.full.pdf]

The predecessor Rivulet1 was published on Neuroinformatics:

[2] Siqi Liu, Donghao Zhang, Sidong Liu, Dagan Feng, Hanchuan Peng, Weidong Cai, 
"Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking", 
Neuroinformatics, Vol.14, Issue 4, pp387-401, 2016.

A C++ implementation of the Rivulet2 algorithm is also available in the lastest [Vaa3D](https://github.com/Vaa3D) sources under the [Rivulet Plugin](https://github.com/Vaa3D/vaa3d_tools/tree/master/released_plugins/v3d_plugins/bigneuron_siqi_rivuletv3d) (Not yet available in the released build). However you can build Vaa3D easily on Mac/Linux following the [Vaa3D wiki](https://github.com/Vaa3D/Vaa3D_Wiki/wiki/Build-Vaa3D-on-Linux) carefully.

## Issues / questions / pull requests

Issues should be reported to the
[Rivuletpy github repository issue tracker](https://github.com/RivuletStudio/rivuletpy/issues).
The ability and speed with which issues can be resolved depends on how complete and
succinct the report is. For this reason, it is recommended that reports be accompanied
with a minimal but self-contained code sample that reproduces the issue, the observed and
expected output, and if possible, the commit ID of the version used. If reporting a
regression, the commit ID of the change that introduced the problem is also extremely valuable
information.

Questions are also welcomed in the [Rivuletpy github repository issue tracker](https://github.com/RivuletStudio/rivuletpy/issues).
If you put on a `question` label. We consider every question as an issue since it means we should have made things clearer/easier for the users.

Pull requests are definitely welcomed! Before you make a pull requests, please kindly create an issue first to discuss the optimal solution.

## Installation

### 0. Setup the Anaconda environment
```
$ conda create -n riv python=python3.6 anaconda # We tested on 3.5 and 3.6. Other python versions >= 3.4 should also work
$ source activate riv
```

### 1. Setup the dependencies
To install rivuletpy with pip, you need to install the following packages manually beforehand since some dependencies of rivuletpy uses them in their setup scripts
* `numpy>=1.8.0`
* `scipy>=0.17.0`
* `Cython>=0.25.1`
* `tqdm-dev`
* `libtiff-dev`
* `SimpleITK`

```
(riv)$ conda install numpy scipy matplotlib cython tqdm git+https://github.com/pearu/pylibtiff.git@e56519a5c2d594102f3ca82c3c14f222d71e0f92#egg=libtiff
(riv)$ conda install -c simpleitk simpleitk  # Install SimpleITK for the support of load mhd
```

### 2. Install Up-to-date Rivuletpy from source
Optionally you can install Rivuletpy from the source files

```
(riv)$ git clone https://github.com/RivuletStudio/rivuletpy.git
(riv)$ cd rivuletpy
(riv)$ python setup.py develop # Needed since we use the fast-forward 'tqdm' and 'pylibtiff'
(riv)$ pip3 install -e .
```

This installs `Rivuletpy` into your `virtualenv` in "editable" mode. That means changes
made to the source code are seen by the installation. To install in read-only mode, omit
the `-e`.

## Test Installation
In ./rivuletpy/
`sh quicktest.sh`

This will download a simple neuron image and perform a neuron tracing with rivulet2 algorithm. If you encountered any issues while installing Rivuletpy, you are welcome to raise an issue for the developers in the [issue tracker](https://github.com/RivuletStudio/rivuletpy/issues)

## Usage
- Reconstruct single neuron file.

The script rtrace command will be installed
```bash
$ rtrace --help
usage: rtrace [-h] -f FILE [-o OUT] [-t THRESHOLD] [-z ZOOM_FACTOR]
              [--save-soma] [--no-save-soma] [--speed]
              [--quality] [--no-quality] [--clean] [--no-clean] [--silent]
              [--no-silent] [-v] [--no-view]
              [--tracing_resolution TRACING_RESOLUTION] [--vtk]

Arguments to perform the Rivulet2 tracing algorithm.

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  The input file. A image file (*.tif, *.nii, *.mat).
  -o OUT, --out OUT     The name of the output file
  -t THRESHOLD, --threshold THRESHOLD
                        threshold to distinguish the foreground and
                        background. Default 0. If threshold<0, otsu will be
                        used.
  -z ZOOM_FACTOR, --zoom_factor ZOOM_FACTOR
                        The factor to zoom the image to speed up the whole
                        thing. Default 1.
  --save-soma           Save the automatically reconstructed soma volume along
                        with the SWC.
  --no-save-soma        Don't save the automatically reconstructed soma volume
                        along with the SWC (default)  
  --speed               Use the input directly as speed image
  --quality             Reconstruct the neuron with higher quality and
                        slightly more computing time
  --no-quality          Reconstruct the neuron with lower quality and slightly
                        more computing time
  --clean               Remove the unconnected segments (default). It is
                        relatively safe to do with the Rivulet2 algorithm
  --no-clean            Keep the unconnected segments
  --silent              Omit the terminal outputs
  --no-silent           Show the terminal outputs & the nice logo (default)
  -v, --view            View the reconstructed neuron when rtrace finishes
  --no-view
  --tracing_resolution TRACING_RESOLUTION
                        Only valid for mhd input files. Will resample the mhd
                        array into isotropic resolution before tracing.
                        Default 1mm
  --vtk                 Store the world coordinate vtk format along with the
                        swc
```

Example Usecases with single neurons in a TIFF image
```
$ rtrace -f example.tif -t 10 # Simple like this. Reconstruct a neuron in example.tif with a background threshold of 10
$ rtrace -f example.tif -t 10 --quality # Better results with longer running time
$ rtrace -f example.tif -t 10 --quality -v # Open a 3D swc viewer after reconstruction 
```

Example Usecases with general tree structures in a mhd image
```
$ rtrace -f example.mhd -t 10 --tracing_resolution 1.5 --vtk # Perform the tracing under an isotropic resolution of 1.5mmx1.5mmx1.5mm and output a vtk output file under the world coordinates along side the swc.
$ rtrace -f example.mhd -t 10 --tracing_resolution 1.5 --vtk --speed # Use the input image directly as the source of making speed image. Recommended if the input mhd is a probablity map of centerlines.
```

Please note that Rivulet2 is powerful of handling the noises, a relatively low intensity threshold is preferred to include all the candidate voxels.

- Compare a swc reconstruction against the manual ground truth
```
$ compareswc --help
usage: compareswc [-h] --target TARGET --groundtruth GROUNDTRUTH
                  [--sigma SIGMA]

Arguments for comparing two swc files.

optional arguments:
  -h, --help            show this help message and exit
  --target TARGET       The input target swc file.
  --groundtruth GROUNDTRUTH
                        The input ground truth swc file.
  --sigma SIGMA         The sigma value to use for the Gaussian function in
                        NetMets.

$ compareswc --target r2_tracing.swc --groundtruth hand_tracing.swc
0.9970 0.8946 0.9865 1 3
```
The `compareswc` command outputs five numbers which are in order: 

precision, recall, f1-score, No. connection error type A, No. connection error type B

## FAQ
### What if I see on Mac OS ```ImportError: Failed to find TIFF library. Make sure that libtiff is installed and its location is listed in PATH|LD_LIBRARY_PATH|..```

Try
```
brew install libtiff
```

### What if I see ```...version `GLIBCXX_3.4.21' not found...``` when I run `rtrace` under Anaconda?
Try
```
(riv)$ conda install libgcc # Upgrades the gcc in your conda environment to the newest
```

### What if I see ```Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.```?
Try to get rid of the mkl in your conda, it has been reported to cause many issues
```
(riv)$ conda install nomkl numpy scipy scikit-learn numexpr
(riv)$ conda remove mkl mkl-service
```

## Dependencies

The build-time and runtime dependencies of Rivuletpy are:

* [numpy](http://www.numpy.org/)
* [scipy](http://www.scipy.org/)
* [Cython](http://cython.org/)
* [scikit-fmm](https://github.com/scikit-fmm)
* [scikit-image](https://github.com/scikit-image)
* [matplotlib](http://www.matplotlib.org/)
* [tqdm](https://github.com/noamraph/tqdm)
* [nibabel](http://nipy.org/nibabel/)