VoxHRNet - Whole Brain Segmentation with Full Volume Neural Network

Related tags

Deep LearningVoxHRNet
Overview

VoxHRNet

This is the official implementation of the following paper:

Whole Brain Segmentation with Full Volume Neural Network

Yeshu Li, Jonathan Cui, Yilun Sheng, Xiao Liang, Jingdong Wang, Eric I-Chao Chang, Yan Xu

Computerized Medical Imaging and Graphics

[arXiv]

Network

architecture

Installation

The following environments/libraries are required:

  • Python 3
  • yacs
  • SimpleITK
  • apex
  • pytorch
  • nibabel
  • numpy
  • scikit-image
  • scipy

Quick Start

Data Preparation

Download the LPBA40 and Hammers n30r95 datasets.

After renaming, your directory tree should look like:

$ROOT
├── data
│   └── LPBA40_N4_RN
│       ├── aseg_TEST001.nii.gz
│       ├── ...
│       ├── aseg_TEST010.nii.gz
│       ├── aseg_TRAIN001.nii.gz
│       ├── ...
│       ├── aseg_TRAIN027.nii.gz
│       ├── aseg_VALIDATE001.nii.gz
│       ├── ...
│       ├── aseg_VALIDATE003.nii.gz
│       ├── orig_TEST001.nii.gz
│       ├── ...
│       ├── orig_TEST010.nii.gz
│       ├── orig_TRAIN001.nii.gz
│       ├── ...
│       ├── orig_TRAIN027.nii.gz
│       ├── orig_VALIDATE001.nii.gz
│       ├── ...
│       └── orig_VALIDATE003.nii.gz
└── VoxHRNet
    ├── voxhrnet.py
    ├── ...
    └── train.py

Create a YACS configuration file and make changes for specific training/test settings accordingly. We use config_lpba.yaml as an example as follows.

Train

Run

python3 train.py --cfg config_lpba.yaml

Test

Run

python3 test.py --cfg config_lpba.yaml

Pretrained Models

For the LPBA40 dataset, we number the subjects from 1-40 alphabetically and split them into 4 folds sequentially. The k-th fold is selected as the test set in the k-th split.

For the Hammers n30r95 dataset, the first 20 subjects and last 10 subjects are chosen as the training and test set respectively.

Their pretrained models can be found in the release page of this repository.

Citation

Please cite our work if you find it useful in your research:

@article{LI2021101991,
title = {Whole brain segmentation with full volume neural network},
journal = {Computerized Medical Imaging and Graphics},
volume = {93},
pages = {101991},
year = {2021},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2021.101991},
url = {https://www.sciencedirect.com/science/article/pii/S0895611121001403},
author = {Yeshu Li and Jonathan Cui and Yilun Sheng and Xiao Liang and Jingdong Wang and Eric I.-Chao Chang and Yan Xu},
keywords = {Brain, Segmentation, Neural networks, Deep learning},
abstract = {Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentation. Learning and inference with incomplete information could lead to sub-optimal final segmentation result. To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume. The framework makes use of complete information in each volume and can be implemented easily. An effective instance in this framework is given subsequently. We adopt the 3D high-resolution network (HRNet) for learning spatially fine-grained representations and the mixed precision training scheme for memory-efficient training. Extensive experiment results on a publicly available 3D MRI brain dataset show that our proposed model advances the state-of-the-art methods in terms of segmentation performance.}
}

Acknowledgement

A large part of the code is borrowed from HRNet.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

You might also like...
BraTs-VNet - BraTS(Brain Tumour Segmentation) using V-Net
BraTs-VNet - BraTS(Brain Tumour Segmentation) using V-Net

BraTS(Brain Tumour Segmentation) using V-Net This project is an approach to dete

Recovering Brain Structure Network Using Functional Connectivity
Recovering Brain Structure Network Using Functional Connectivity

Recovering-Brain-Structure-Network-Using-Functional-Connectivity Framework: Papers: This repository provides a PyTorch implementation of the models ad

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish
PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

GLOM TensorFlow This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups transformers, neu

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation Official PyTroch implementation of HPRNet. HPRNet: Hierarchical Point Regre

minimizer-space de Bruijn graphs (mdBG) for whole genome assembly

rust-mdbg: Minimizer-space de Bruijn graphs (mdBG) for whole-genome assembly rust-mdbg is an ultra-fast minimizer-space de Bruijn graph (mdBG) impleme

Official implementation of the paper Visual Parser: Representing Part-whole Hierarchies with Transformers
Official implementation of the paper Visual Parser: Representing Part-whole Hierarchies with Transformers

Visual Parser (ViP) This is the official implementation of the paper Visual Parser: Representing Part-whole Hierarchies with Transformers. Key Feature

CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching(CVPR2021)

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.
Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.

Hand Gesture Volume Controller Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out). Code Firstly I have created a

Comments
  • How to get the LPBA40_N4_RN dataset for the example

    How to get the LPBA40_N4_RN dataset for the example

    Thanks for your great work. I'm trying to run the example but stuck by the dataset. It seems there are multiple LPBA40 datasets on the give site LPBA40, and the data file format are not nii as in the example. Is there a downloadable LPBA40_N4_RN dataset or could you give some details on how to generate the dataset in the example?

    opened by mgcyung 2
  • ACTION REQUIRED: Microsoft needs this private repository to complete compliance info

    ACTION REQUIRED: Microsoft needs this private repository to complete compliance info

    There are open compliance tasks that need to be reviewed for your VoxHRNet repo.

    Action required: 4 compliance tasks

    To bring this repository to the standard required for 2021, we require administrators of this and all Microsoft GitHub repositories to complete a small set of tasks within the next 60 days. This is critical work to ensure the compliance and security of your microsoft GitHub organization.

    Please take a few minutes to complete the tasks at: https://repos.opensource.microsoft.com/orgs/microsoft/repos/VoxHRNet/compliance

    • The GitHub AE (GitHub inside Microsoft) migration survey has not been completed for this private repository
    • No Service Tree mapping has been set for this repo. If this team does not use Service Tree, they can also opt-out of providing Service Tree data in the Compliance tab.
    • No repository maintainers are set. The Open Source Maintainers are the decision-makers and actionable owners of the repository, irrespective of administrator permission grants on GitHub.
    • Classification of the repository as production/non-production is missing in the Compliance tab.

    You can close this work item once you have completed the compliance tasks, or it will automatically close within a day of taking action.

    If you no longer need this repository, it might be quickest to delete the repo, too.

    GitHub inside Microsoft program information

    More information about GitHub inside Microsoft and the new GitHub AE product can be found at https://aka.ms/gim.

    FYI: current admins at Microsoft include @scarlett2018, @EricChangMSR, @simon1727

    opened by microsoft-github-operations[bot] 0
Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

69 Dec 10, 2022
Automatically replace ONNX's RandomNormal node with Constant node.

onnx-remove-random-normal This is a script to replace RandomNormal node with Constant node. Example Imagine that we have something ONNX model like the

Masashi Shibata 1 Dec 11, 2021
🎯 A comprehensive gradient-free optimization framework written in Python

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

Devin Soni 565 Dec 26, 2022
code for paper -- "Seamless Satellite-image Synthesis"

Seamless Satellite-image Synthesis by Jialin Zhu and Tom Kelly. Project site. The code of our models borrows heavily from the BicycleGAN repository an

Light 14 Apr 05, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
Datasets and pretrained Models for StyleGAN3 ...

Datasets and pretrained Models for StyleGAN3 ... Dear arfiticial friend, this is a collection of artistic datasets and models that we have put togethe

lucid layers 34 Oct 06, 2022
Keras implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

Erik Linder-Norén 8.9k Jan 04, 2023
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
Stochastic gradient descent with model building

Stochastic Model Building (SMB) This repository includes a new fast and robust stochastic optimization algorithm for training deep learning models. Th

S. Ilker Birbil 22 Jan 19, 2022
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

English | 简体中文 Latest News 2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021. 2021.07.29 PaddleHeli

633 Jan 04, 2023
[ECCV 2020] Gradient-Induced Co-Saliency Detection

Gradient-Induced Co-Saliency Detection Zhao Zhang*, Wenda Jin*, Jun Xu, Ming-Ming Cheng ⭐ Project Home » The official repo of the ECCV 2020 paper Grad

Zhao Zhang 35 Nov 25, 2022
Adversarial Autoencoders

Adversarial Autoencoders (with Pytorch) Dependencies argparse time torch torchvision numpy itertools matplotlib Create Datasets python create_datasets

Felipe Ducau 188 Jan 01, 2023
Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor

Lukas Hedegaard 21 Dec 22, 2022
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

3k Jan 08, 2023
CCP dataset from Clothing Co-Parsing by Joint Image Segmentation and Labeling

Clothing Co-Parsing (CCP) Dataset Clothing Co-Parsing (CCP) dataset is a new clothing database including elaborately annotated clothing items. 2, 098

Wei Yang 434 Dec 24, 2022
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

Generative Image Inpainting An open source framework for generative image inpainting task, with the support of Contextual Attention (CVPR 2018) and Ga

2.9k Dec 16, 2022
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021)

RSCD (BS-RSCD & JCD) Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021) by Zhihang Zhong, Yinqiang Zheng, Imari Sato We co

81 Dec 15, 2022
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Ad^2Attack:Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se

Intelligent Vision for Robotics in Complex Environment 10 Nov 07, 2022
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Computational Photography Lab @ SFU 1.1k Jan 02, 2023
The all new way to turn your boring vector meshes into the new fad in town; Voxels!

Voxelator The all new way to turn your boring vector meshes into the new fad in town; Voxels! Notes: I have not tested this on a rotated mesh. With fu

6 Feb 03, 2022