The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

Overview

DAGAN

This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction published in IEEE Transactions on Medical Imaging (2018).
Guang Yang*, Simiao Yu*, et al.
(* equal contributions)

If you use this code for your research, please cite our paper.

@article{yang2018_dagan,
	author = {Yang, Guang and Yu, Simiao and Dong, Hao and Slabaugh, Gregory G. and Dragotti, Pier Luigi and Ye, Xujiong and Liu, Fangde and Arridge, Simon R. and Keegan, Jennifer and Guo, Yike and Firmin, David N.},
	journal = {IEEE Trans. Med. Imaging},
	number = 6,
	pages = {1310--1321},
	title = {{DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction}},
	volume = 37,
	year = 2018
}

If you have any questions about this code, please feel free to contact Simiao Yu ([email protected]).

Prerequisites

The original code is in python 3.5 under the following dependencies:

  1. tensorflow (v1.1.0)
  2. tensorlayer (v1.7.2)
  3. easydict (v1.6)
  4. nibabel (v2.1.0)
  5. scikit-image (v0.12.3)

Code tested in Ubuntu 16.04 with Nvidia GPU + CUDA CuDNN (whose version is compatible to tensorflow v1.1.0).

How to use

  1. Prepare data

    1. Data used in this work are publicly available from the MICCAI 2013 grand challenge (link). We refer users to register with the grand challenge organisers to be able to download the data.
    2. Download training and test data respectively into data/MICCAI13_SegChallenge/Training_100 and data/MICCAI13_SegChallenge/Testing_100 (We randomly included 100 T1-weighted MRI datasets for training and 50 datasets for testing)
    3. run 'python data_loader.py'
    4. after running the code, training/validation/testing data should be saved to 'data/MICCAI13_SegChallenge/' in pickle format.
  2. Download pretrained VGG16 model

    1. Download 'vgg16_weights.npz' from this link
    2. Save 'vgg16_weights.npz' into 'trained_model/VGG16'
  3. Train model

    1. run 'CUDA_VISIBLE_DEVICES=0 python train.py --model MODEL --mask MASK --maskperc MASKPERC' where you should specify MODEL, MASK, MASKPERC respectively:
    • MODEL: choose from 'unet' or 'unet_refine'
    • MASK: choose from 'gaussian1d', 'gaussian2d', 'poisson2d'
    • MASKPERC: choose from '10', '20', '30', '40', '50' (percentage of mask)
  4. Test trained model

    1. run 'CUDA_VISIBLE_DEVICES=0 python test.py --model MODEL --mask MASK --maskperc MASKPERC' where you should specify MODEL, MASK, MASKPERC respectively (as above).

Results

Please refer to the paper for the detailed results.

Owner
TensorLayer Community
A neutral open community to promote AI technology.
TensorLayer Community
Mscp jamf - Build compliance in jamf

mscp_jamf Build compliance in Jamf. This will build the following xml pieces to

Bob Gendler 3 Jul 25, 2022
Code to produce syntactic representations that can be used to study syntax processing in the human brain

Can fMRI reveal the representation of syntactic structure in the brain? The code base for our paper on understanding syntactic representations in the

Aniketh Janardhan Reddy 4 Dec 18, 2022
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Pavel Izmailov 25 Sep 21, 2022
A lightweight deep network for fast and accurate optical flow estimation.

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation The official PyTorch implementation of FastFlowNet (ICRA 2021). Authors: Lingtong

Tone 161 Jan 03, 2023
An Object Oriented Programming (OOP) interface for Ontology Web language (OWL) ontologies.

Enabling a developer to use Ontology Web Language (OWL) along with its reasoning capabilities in an Object Oriented Programming (OOP) paradigm, by pro

TheEngineRoom-UniGe 7 Sep 23, 2022
A list of Machine Learning Art Colabs

ML Visual Art Colabs A list of cool Colabs on Machine Learning Imagemaking or other artistic purposes 3D Ken Burns Effect Ken Burns Effect by Manuel R

Derrick Schultz (he/him) 789 Dec 12, 2022
A generator of point clouds dataset for PyPipes.

CloudPipesGenerator Documentation | Colab Notebooks | Video Tutorials | Master Degree website A generator of point clouds dataset for PyPipes. TODO Us

1 Jan 13, 2022
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
Fast RFC3339 compliant Python date-time library

udatetime: Fast RFC3339 compliant date-time library Handling date-times is a painful act because of the sheer endless amount of formats used by people

Simon Pirschel 235 Oct 25, 2022
Embodied Intelligence via Learning and Evolution

Embodied Intelligence via Learning and Evolution This is the code for the paper Embodied Intelligence via Learning and Evolution Agrim Gupta, Silvio S

Agrim Gupta 111 Dec 13, 2022
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information This repository contains code, model, dataset for ChineseBERT at ACL2021. Ch

413 Dec 01, 2022
A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi

LSTM-Time-Series-Prediction A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi Contest. The Link of the Cont

KevinCHEN 1 Jun 13, 2022
A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

A Pytorch Implementation of Source data‐free domain adaptation of object detector through domain‐specific perturbation Please follow Faster R-CNN and

1 Dec 25, 2021
A set of tools for converting a darknet dataset to COCO format working with YOLOX

darknet格式数据→COCO darknet训练数据目录结构(详情参见dataset/darknet): darknet ├── class.names ├── gen_config.data ├── gen_train.txt ├── gen_valid.txt └── images

RapidAI-NG 148 Jan 03, 2023
Explanatory Learning: Beyond Empiricism in Neural Networks

Explanatory Learning This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks". Datasets Download the datasets

GLADIA Research Group 10 Dec 06, 2022
Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Kento Nishi 22 Jul 07, 2022
The code release of paper Low-Light Image Enhancement with Normalizing Flow

[AAAI 2022] Low-Light Image Enhancement with Normalizing Flow Paper | Project Page Low-Light Image Enhancement with Normalizing Flow Yufei Wang, Renji

Yufei Wang 176 Jan 06, 2023
A smart Chat bot that can help to know about corona virus and Make prediction of corona using X-ray.

TRINIT_Hum_kuchh_nahi_karenge_ML01 Document Link https://github.com/Jatin-Goyal-552/TRINIT_Hum_kuchh_nahi_karenge_ML01/blob/main/hum_kuchh_nahi_kareng

JatinGoyal 1 Feb 03, 2022
《Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching》(CVPR 2020)

This contains the codes for cross-view geo-localization method described in: Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching, CVPR2020.

41 Oct 27, 2022
Securetar - A streaming wrapper around python tarfile and allow secure handling files and support encryption

Secure Tar Secure Tarfile library It's a streaming wrapper around python tarfile

Pascal Vizeli 2 Dec 09, 2022