This is the official repository of Deep Learning Based Brain Tumor Segmentation: A Survey.
The very first draft is open on [arxiv].
Zhihua Liu,
Lei Tong,
Zheheng Jiang,
Long Chen,
Feixiang Zhou,
Qianni Zhang,
Xiangrong Zhang,
Yaochu Jin,
and Huiyu Zhou.
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.
Please feel free to make contact or open issues if you want to add results, discuss or give suggestions.
Survey Title | Venue | Year | Remarks |
---|---|---|---|
Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012--2018 challenges | IEEE Reviews in Biomedical Engineering | 2019 | A review of challenge submissions of BraTS from 2012-2018. |
A survey on brain tumor detection using image processing techniques | 2017 7th International Conference on Cloud computing, Data science & Engineering-confluence | 2017 | A review of general brain tumor segmentation methods. |
Survey of brain tumor segmentation techniques on magnetic resonance imaging | Nano Biomedicine and Engineering | 2019 | A general summary of classic brain tumor segmentation methods. |
State of the art survey on MRI brain tumor segmentation | Magnetic Resonance Imaging | 2013 | Review on convolutional neural networks used for brain MRI image analysis. |
A survey of MRI-based brain tumor segmentation methods | Tsinghua Science and Technology | 2014 | Review on MRI based brain tumor segmentation methods. |
Data augmentation for brain-tumor segmentation: a review | Frontiers in Computational Neuroscience | 2019 | Analysed the technical details and impacts of different kinds of data augmentation methods with the application to brain tumor segmentation. |
A survey on deep learning in medical image analysis | Medical Image Analysis | 2017 | A comprehensive review on deep learning based medical image analysis. |
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review | Artificial Intelligence in Medicine | 2018 | A review on use of deep convolutional neural networks for brain image analysis. |
Deep learning for brain MRI segmentation: state of the art and future directions | Journal of Digital Imaging | 2017 | A survey on deep learning for brain MRI segmentation. |
A guide to deep learning in healthcare | Nature Medicine | 2019 | A survey on deep learning for health-care. |
Deep learning for generic object detection: A survey | International Journal of Computer Vision | 2020 | A comprehensive review on deep learning based object detection. |
Deep learning | Nature | 2015 | An introduction review on deep learning and its application. |
Recent advances in convolutional neural networks | Pattern Recognition | 2018 | A survey on convolutional neural networksand its application on computer vision, language processing and speech. |
Deep Learning Based Brain Tumor Segmentation: A Survey | Ours | - | A comprehensive survey of deep learning based brain tumor segmentation. |
Title | First Author | Paper Link | Code Link |
---|---|---|---|
Brain tumor segmentation with Deep Neural Networks | Mohammad Havaei | Paper | Code (3rd Party) |
DeepMedic on Brain Tumor Segmentation | Konstantinos Kamnitsas | Paper | Code |
Multi-dimensional Gated Recurrent Units for Brain Tumor Segmentation | Simon Andermatt | Paper | Code |
Volumetric Multimodality Neural Network For Brain Tumor Segmentation | Laura Silvana Castillo | Paper | Code |
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge | Fabian Isensee | Paper | Code (3rd Party) |
Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation | Kamlesh Pawar | Paper | Code |
Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks | Guotai Wang | Paper | Code |
No New-Net | Fabian Isensee | Paper | Code |
3D MRI Brain Tumor Segmentation Using Autoencoder Regularization | Andriy Myronenko | Paper | Code |
3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation | Nicholas Nuechterlein | Paper | Code |
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation | Chenhong Zhou | Paper1/Paper2 | Code |
Multi-step Cascaded Networks for Brain Tumor Segmentation | Xiangyu Li | Paper | Code |
An Ensemble of 2D Convolutional Neural Network for 3D Brain Tumor Segmentation | Kamlesh Pawar | Paper | Code |
Knowledge Distillation for Brain Tumor Segmentation | Dmitrii Lachinov | Paper | Code |
Label-Efficient Multi-Task Segmentation using Contrastive Learning | Junichiro Iwasawa | Paper | Code |
Vox2Vox: 3D-GAN for Brain Tumour Segmentation | Marco Domenico Cirillo | Paper | Code |
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution | Theophraste Henry | Paper | Code |
Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images | Vaanathi Sundaresan | Paper | Code |
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation | Chenggang Lyu | Paper | Code |
HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation | Zhengrong Luo | Paper | Code |
3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI | Chen Chen | Paper | Code |
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer | Wenxuan Wang | Paper | Code |
If you find our work useful in your research, please consider citing:
@article{liu2020deep,
title={Deep learning based brain tumor segmentation: a survey},
author={Liu, Zhihua and Chen, Long and Tong, Lei and Zhou, Feixiang and Jiang, Zheheng and Zhang, Qianni and Shan, Caifeng and Wang, Yinhai and Zhang, Xiangrong and Li, Ling and Huiyu Zhou},
journal={arXiv preprint arXiv:2007.09479},
year={2020}
}
The authors thank Prof. Guotai Wang, Prof. Dingwen Zhang and Dr. Tongxue Zhou for their detailed feedbacks and suggestions.
This code is made available under the GPLv3 License and is available for non-commercial academic purposes.