3d-unet | Chainer implementations of 3D UNet | Machine Learning library

by shiba24 Python Version: Current License: No License

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kandi X-RAY | 3d-unet Summary

3d-unet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. 3d-unet has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This repository includes Tensorflow (v1.0), PyTorch, and Chainer (v2.0) implementations of 3D UNet, semantic segmentation neural network for 3D voxel data. NOTE: This is not official implementation. Currently only Chainer implementation works well. The original paper is: Özgün Çiçek, Ahmed Abdulkadir, S. Lienkamp, Thomas Brox & Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9901, 424--432, Oct 2016.

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3d-unet has a low active ecosystem.

It has 163 star(s) with 58 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 3 have been closed. On average issues are closed in 27 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of 3d-unet is current.

Quality

3d-unet has 0 bugs and 0 code smells.

Security

3d-unet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

3d-unet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

3d-unet does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

3d-unet 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.

Top functions reviewed by kandi - BETA

kandi has reviewed 3d-unet and discovered the below as its top functions. This is intended to give you an instant insight into 3d-unet implemented functionality, and help decide if they suit your requirements.

Train the model
Store prediction
Calculate minibatch stats
Restore model from file
Save the model to the given path
Compute the error rate
Logs the average loss
Create convolution network
Get image summary
Runs prediction on the model

Get all kandi verified functions for this library.

3d-unet Key Features

No Key Features are available at this moment for 3d-unet.

3d-unet Examples and Code Snippets

No Code Snippets are available at this moment for 3d-unet.

Community Discussions

Trending Discussions on 3d-unet

Training a CNN with multiple input 3D-arrays in keras

QUESTION

Training a CNN with multiple input 3D-arrays in keras

Asked 2020-Mar-31 at 18:46

I need to train a 3D_Unet model with (128x128x128) patches of 42 CT scans.

My input data is 128x128x128 for the CT scans and also for masks. I extended the shape of arrays to (128, 128, 128, 1). Where 1 is the channel.

The problem is how to feed the model with my list of 40 4D-arrays?

How can I use the model.fit() or model.train_on_batch with the correct input shape specified in my Model?

...

ANSWER

Answered 2020-Mar-31 at 18:46

You have to transform your list of numpy arrays of shape (128, 128, 128, 1) into a stacked 5 dimensional numpy array of shape (42, 128, 128, 128, 1). You can do this with: model.fit(np.array(train_arrays_list), np.array(mask_arrays_list), batch_size=1, ...)

Source https://stackoverflow.com/questions/60956371

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

Vulnerabilities

No vulnerabilities reported

Install 3d-unet

You can download it from GitHub.
You can use 3d-unet 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 repository includes Tensorflow (v1.0), PyTorch, and Chainer (v2.0) implementations of 3D UNet, semantic segmentation neural network for 3D voxel data. NOTE: This is not official implementation. Currently only Chainer implementation works well. The original paper is: Özgün Çiçek, Ahmed Abdulkadir, S. Lienkamp, Thomas Brox & Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9901, 424--432, Oct 2016.

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