WORD: Revisiting Organs Segmentation in the Whole Abdominal Region

Overview

WORD: Revisiting Organs Segmentation in the Whole Abdominal Region (Paper and DataSet).

  • [New] Note that all the emails about the download permission of WORD will be handled after the paper is accepted, all information will be updated in time in this repo, please don't send them multiple times!!!
  • This repo provides the codebase and dataset of work WORD: Revisiting Organs Segmentation in the Whole Abdominal Region, and the download requirement will be approved after the paper is accepted, stay tuned !!!
  • Now, we are preparing an online evaluation server for the fair and open research if you have experience with it or want to join or provide some support to this project, please contact us !!!
  • Some information about the WORD dataset is presented in the following:
Fig. 1. An example in the WORD dataset.
Fig. 2. Volume distribution or each organ in the WORD dataset.
Fig. 3. User study based on three junior oncologists independently, each of them comes from a different hospital.

DataSet

Please contact Xiangde (luoxd1996 AT gmail DOT com) for the dataset. Two steps are needed to download and access the dataset: 1) using your google email to apply for the download permission; 2) using your affiliation email to get the unzip password. We will get back to you after the paper is accepted. We just handle the real-name email and your email suffix must match your affiliation. The email should contain the following information:

Name/Homepage/Google Scholar: (Tell us who you are.)
Primary Affiliation: (The name of your institution or university, etc.)
Job Title: (E.g., Professor, Associate Professor, Ph.D., etc.)
Affiliation Email: (the password will be sent to this email, we just reply to the email which is the end of "edu".)
How to use: (Only for academic research, not for commercial use or second-development.)

In addition, this work is still ongoing, the WORD dataset will be extended to larger and more diverse (more patients, more organs, and more modalities, more clinical hospitals' data and MR Images will be considered to include future), any suggestion, comment, collaboration, and sponsor are welcome.

Acknowledgment and Statement

  • This dataset belongs to the Healthcare Intelligence Laboratory at University of Electronic Science and Technology of China and is licensed under the GNU General Public License v3.0.
  • This project has been approved by the privacy and ethical review committee. We thank all collaborators for the data collection, annotation, checking, and user study!
  • This project and dataset were designed for open-available academic research, not for clinical, commercial, second-development, or other use. In addition, if you used it for your academic research, you are encouraged to release the code and the pre-trained model.
  • The interesting and memorable name WORD is suggested by Dr. Jie-Neng, thanks a lot !!!

Citation

It would be highly appreciated if you cite our paper when using the WORD dataset or code:

@article{luo2021word,
  title={{WORD}: Revisiting Organs Segmentation in the Whole Abdominal Region},
  author={Luo, Xiangde and Liao, Wenjun and Xiao, Jianghong and Song, Tao and Zhang, Xiaofan and Li, Kang and Wang, Guotai and Zhang, Shaoting},
  journal={arXiv preprint arXiv:2111.02403},
  year={2021}
}
Owner
Healthcare Intelligence Laboratory
Healthcare Intelligence Laboratory
FairFuzz: AFL extension targeting rare branches

FairFuzz An AFL extension to increase code coverage by targeting rare branches. FairFuzz has a particular advantage on programs with highly nested str

Caroline Lemieux 222 Nov 16, 2022
Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks.

Heterogeneous Graph Benchmark Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks. Roadmap We organize our repo by task, and on

THUDM 176 Dec 17, 2022
Python package for missing-data imputation with deep learning

MIDASpy Overview MIDASpy is a Python package for multiply imputing missing data using deep learning methods. The MIDASpy algorithm offers significant

MIDASverse 77 Dec 03, 2022
A lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look At CoefficienTs)

Real-time Instance Segmentation and Lane Detection This is a lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look

Jin 4 Dec 30, 2022
[CVPRW 2021] Code for Region-Adaptive Deformable Network for Image Quality Assessment

RADN [CVPRW 2021] Code for Region-Adaptive Deformable Network for Image Quality Assessment [Paper on arXiv] Overview Update [2021/5/7] add codes for W

IIGROUP 53 Dec 28, 2022
[CVPR 2020] Interpreting the Latent Space of GANs for Semantic Face Editing

InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing Figure: High-quality facial attributes editing results with InterFaceGA

GenForce: May Generative Force Be with You 1.3k Dec 29, 2022
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

The first comprehensive Robustness investigation benchmark on large-scale dataset ImageNet regarding ARchitecture design and Training techniques towards diverse noises.

132 Dec 23, 2022
Attention mechanism with MNIST dataset

[TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention ma

YeongHyeon Park 12 Jun 10, 2022
Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

SSC-GAN_repo Pytorch implementation for 'Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation'.PDF SSC-GAN:Sem

tyty 4 Aug 28, 2022
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Zicong Fan 40 Dec 22, 2022
Forecasting with Gradient Boosted Time Series Decomposition

ThymeBoost ThymeBoost combines time series decomposition with gradient boosting to provide a flexible mix-and-match time series framework for spicy fo

131 Jan 08, 2023
FasterAI: A library to make smaller and faster models with FastAI.

Fasterai fasterai is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks

Nathan Hubens 193 Jan 01, 2023
This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge column damage detection

Bridge-damage-segmentation This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge c

Jingxiao Liu 5 Dec 07, 2022
Unsupervised captioning - Code for Unsupervised Image Captioning

Unsupervised Image Captioning by Yang Feng, Lin Ma, Wei Liu, and Jiebo Luo Introduction Most image captioning models are trained using paired image-se

Yang Feng 207 Dec 24, 2022
Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.

Vehicle Detection Video demo Overview Vehicle detection using these machine learning and computer vision techniques. Linear SVM HOG(Histogram of Orien

hata 1.1k Dec 18, 2022
This is the dataset for testing the robustness of various VO/VIO methods

KAIST VIO dataset This is the dataset for testing the robustness of various VO/VIO methods You can download the whole dataset on KAIST VIO dataset Ind

1 Sep 01, 2022
AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition.

AnimalAI 3 AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition. It aims to support AI research t

Matthew Crosby 58 Dec 12, 2022
Python Fanduel API (2021) - Lineup Automation

Southpaw is a python package that provides access to the Fanduel API. Optimize your DFS experience by programmatically updating your lineups, analyzin

Brandin Canfield 13 Jan 04, 2023
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Bin Xiao 175 Jan 08, 2023