Deep Learning Based Brain Tumor Segmentation: A Survey

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.

Introduction

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.

Taxonomy

(A) Designing Effective Segmentation Networks

Result Comparison

(B) Segmentation under Imbalanced Condition

Result Comparison

(C) Utilising Multi Modality Information

Result Comparison

Progress in Past Decades

Related Survey Papers

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.

Paper with Souce Code Links

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

Citation

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}
}

Acknowledgement

The authors thank Prof. Guotai Wang, Prof. Dingwen Zhang and Dr. Tongxue Zhou for their detailed feedbacks and suggestions.

License

This code is made available under the GPLv3 License and is available for non-commercial academic purposes.