Giter Site home page Giter Site logo

collagan_mri's Introduction

Collaborative Generative Adversarial Networks for Missing MR contrast imputation

An implementation of "Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN", arXiv:1905.04105

The main codes have two parts: one is Collaborative Generative Adversarial Networks for MR contrast imputation problem and the other is brain tumor segmentation network. The Collaborative GAN is a deep learning model for missing image data imputation (Dongwook Lee et al. CVPR 2019. oral). The concept for the missing image imputation is applied for MR contrast problem and this is the implementation of that using tensorflow. The segmentation code is the modified version of 3D MRI brain tumor segmentation using autoencoder regularization(Andriy Myronenko, 2018, arXiv:1810.11654) due to the limited GPU memory issue.

This repository provides a tensorflow implementation of CollaGAN for missing MR contrast imputation as described in the paper:

Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN, Dongwook Lee, Won-Jin Moon, Jong Chul Ye (arXiv:1905.04105) [Paper]

OS

The package development version is tested on Linux operating systems. The developmental version of the package has been tested on the following systems: Linux: Ubuntu 16.04

Requirements

The codebase is implemented in Python 3.5.2. package versions used for development are just below.

tensorflow 		  1.10.1
tqdm			  4.28.1
numpy			  1.14.5
scipy			  1.1.0
argparse		  1.1
logging 	 	  0.5.1.2
ipdb 			  0.11
cv2 			  3.4.3

Datasets

Dataset for Multimodal brain tumor segmentation challenge BRATS2015 (https://www.smir.ch/BRATS/Start2015)

Main train files

train.py
seg_train.py

These files are handled by the scripts/train_CollaGAN_BRATS.sh and scripts/train_Segmentation_BRATS.sh files with following commands:

sh scripts/train_CollaGAN_BRATS.sh
sh scripts/train_Segmentation_BRATS.sh

Input and output options

The explanation of the input and output options for CollaGAN model and Segmentation model for training are introduced in following files, respectively:

options/colla_options.py
options/seg_option.py

collagan_mri's People

Contributors

bispl-kaist avatar dwleekaist avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar

collagan_mri's Issues

NumPy file

I'm trying to use your code but I don't know how you create the NumPy file with filename of the slices in the class Brats. Any advice/instruction?

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.