Overview of Multi-Modal Brain Tumor MR Image Segmentation

Healthcare

Sahraoui Dhelimd¹  Liang Wu²  Wenyin Zhang³  Shunbo Hu³  Yong Wu³  Bo Yang⁴ 

[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;School of Control Science and Engineering, Shandong University, Jinan 250061, China;School of Information Science and Engineering, Linyi University, Linyi 276000, China;Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan 250022, China;

关键词: image segmentation;    brain tumor;    magnetic resonance imaging;    multi-modality;   

DOI  :  10.3390/healthcare9081051

来源: DOAJ

【 摘 要 】

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

【 授权许可】

Unknown