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

Rituraj Dutta 7 Nov 27, 2022
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

null 5 Nov 30, 2022
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

null 1 May 31, 2022
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

Rishit Dagli 32 Feb 21, 2022
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

Nermin Samet 53 Dec 4, 2022
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

Barış Ekim 148 Dec 1, 2022
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

Shuyang Sun 117 Dec 11, 2022
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

null 106 Dec 28, 2022
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

Tejas Prajapati 16 Sep 11, 2021
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
GitHub Repository
Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction".

TGIN Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction". Files in the folder dataset/ electr

Alibaba 21 Dec 21, 2022
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
Hand gesture recognition model that can be used as a remote control for a smart tv.

Gesture_recognition The training data consists of a few hundred videos categorised into one of the five classes. Each video (typically 2-3 seconds lon

Pratyush Negi 1 Aug 11, 2022
This is an official implementation for "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"

DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation This repo is the official implementation of "DeciWatch: A Simple Baseline for

117 Dec 24, 2022
Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Pano3D A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation Pano3D is a new benchmark for depth estimation from spherical panoramas. We

Visual Computing Lab, Information Technologies Institute, Centre for Reseach and Technology Hellas 50 Dec 29, 2022
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation This is an unofficial PyTorch

MINDs Lab 170 Jan 04, 2023
Pytorch implementation of Learning Rate Dropout.

Learning-Rate-Dropout Pytorch implementation of Learning Rate Dropout. Paper Link: https://arxiv.org/pdf/1912.00144.pdf Train ResNet-34 for Cifar10: r

42 Nov 25, 2022
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

MADGRAD Optimization Method A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization pip install madgrad Try it out! A best

Meta Research 774 Dec 31, 2022
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

SAFL: A Self-Attention Scene Text Recognizer with Focal Loss This repository implements the SAFL in pytorch. Installation conda env create -f environm

6 Aug 24, 2022
This is a code repository for paper OODformer: Out-Of-Distribution Detection Transformer

OODformer: Out-Of-Distribution Detection Transformer This repo is the official the implementation of the OODformer: Out-Of-Distribution Detection Tran

34 Dec 02, 2022
A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Project Name: Which Bollywood Celebrity You look like A Deep learning based streamlit web app which can tell with which bollywood celebrity your face

BAPPY AHMED 20 Dec 28, 2021
PyTorch implementation of Off-policy Learning in Two-stage Recommender Systems

Off-Policy-2-Stage This repo provides a PyTorch implementation of the MovieLens experiments for the following paper: Off-policy Learning in Two-stage

Jiaqi Ma 25 Dec 12, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
A deep learning CNN model to identify and classify and check if a person is wearing a mask or not.

Face Mask Detection The Model is designed to check if any human is wearing a mask or not. Dataset Description The Dataset contains a total of 11,792 i

1 Mar 01, 2022
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [2021]

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations This repo contains the Pytorch implementation of our paper: Revisit

Wouter Van Gansbeke 80 Nov 20, 2022
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022
Code for Greedy Gradient Ensemble for Visual Question Answering （ICCV 2021, Oral）

Greedy Gradient Ensemble for De-biased VQA Code release for "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). GGE can

21 Jun 29, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022