A survey of MRI-based brain tumor segmentation methods

By Liu Jin, Li Min, Jian-xin Wang, Fang-Xiang Wu, Tianming Liu, Pan Yi. 2014

406 citations recorded by the source corpus.

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI-based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/dc169f30f62d57f3785090e33ef1875df5124ec5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Caifeng Shan
2. Zheheng Jiang
3. Zhihua Liu
4. Huiyu Zhou
5. Mukhriddin Mukhiddinov
6. Lei Tong
7. Akmalbek Bobomirzaevich Abdusalomov
8. Long Chen

In Neurology

1. Mukhriddin Mukhiddinov
2. Akmalbek Bobomirzaevich Abdusalomov
3. T. Whangbo

Citations in the corpus

• Deep learning based brain tumor segmentation: a survey (2020, methodology): “State of the art survey on MRI brain tumor segmentation [17] MRI 2013 Summary of segmentation methods before 2013 A survey of MRI-based brain tumor segmentation methods [18] Tsinghua Science and Technology 2014 Survey of methods used for brain MRI segmentation before 2014 A survey on deep learning i”
• Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, background): “In order to achieve correct segmentation, many existing tumor segmentation algorithms [59] require extensive quantities of training data, and they may struggle to deal with unknown tumor types in supervised learning settings.”