vesseg | Brain vessel segmentation | Machine Learning library

by fepegar Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | vesseg Summary

vesseg is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. vesseg has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Brain vessel segmentation from digital subtraction angiography (DSA) using a 3D convolutional neural network (CNN). (Brain parcellation performed using GIF, not included in this repository).

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Quality

Security

License

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Support

vesseg has a low active ecosystem.

It has 20 star(s) with 10 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 5 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vesseg is current.

Quality

vesseg has no bugs reported.

Security

vesseg has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

vesseg is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

vesseg releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed vesseg and discovered the below as its top functions. This is intended to give you an instant insight into vesseg implemented functionality, and help decide if they suit your requirements.

Resample the image
Merge multiple images together
Resample a floating point image
Resample an image
Download weights and train weights
Download weights
Convert an image to a utk
Run NiftySegment
Runs marching cubes
Pad image using sitk
Check if the image has border
Draw marching cubes from an image
Keep the largest component in an image
Convolutional layer op

Get all kandi verified functions for this library.

vesseg Key Features

No Key Features are available at this moment for vesseg.

vesseg Examples and Code Snippets

No Code Snippets are available at this moment for vesseg.

Community Discussions

Trending Discussions on vesseg

NiftyNet ValueError: Dimension -1 must be >= 0

QUESTION

NiftyNet ValueError: Dimension -1 must be >= 0

Asked 2019-May-28 at 12:29

I want to train a VNet for a segmentation task. For a simple "getting started" example, I have one CT and its corresponding segmentation image.

I get a ValueError when running the train command:

...

ANSWER

Answered 2018-May-02 at 10:37

I added num_classes = 2 to the [SEGMENTATION] section of my config.ini and the error is now gone.

Source https://stackoverflow.com/questions/50101695

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install vesseg

You can download it from GitHub.
You can use vesseg like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

This package uses NiftyNet, which is built on top of TensorFlow, so first of all you need to follow the instructions to setup your NVIDIA GPU. While it is possible to perform a segmentation without a GPU, inference of one large volume (512 × 512 × 394 voxels) using the default parameters takes one hour using CPU only and around 90 seconds using a GPU.

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