Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (8): 81-87.doi: 10.6040/j.issn.1671-7554.0.2020.0598

• Special Topic on Brain Science and Brain Like Intelligence • Previous Articles     Next Articles

A research on multi-modal MRI analysis based on machine learning for brain glioma

Qiang WU1,2,*(),Zekun HE1,Ju LIU1,2,Xiaomeng CUI1,Shuang SUN1,Wei SHI1   

  1. 1. School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China
    2. Institute of Brain and Brain-Inspired Science, Shandong University, Jinan 250012, Shandong, China
  • Received:2020-04-15 Online:2020-08-07 Published:2020-08-07
  • Contact: Qiang WU E-mail:wuqiang@sdu.edu.cn

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Abstract:

Brain glioma, a common primary brain tumor, has characteristics of high recurrence rate, high death rate and low cure rate. Conventional clinical diagnosis mainly depends on CT and MRI. With the development of imaging technology and machine learning methods, multi-modal image intelligent analysis technology has gradually become a research hotspot, which has an important application prospect in brain glioma lesion segmentation and measurement, tumor classification, overall survival prediction and genotype identification. This paper updates the application of machine learning and multi-modal imaging in the clinical diagnosis and prognosis of brain glioma.

Key words: Brain Glioma, Machine Learning, Multi-modal MRI, Image lesion segmentation, Survival prediction, Genotype prediction

CLC Number: 

  • R574

Fig.1

Schematic diagram of U-Net architecture"

Fig.2

Schematic diagram of multiple feature extraction network architecture"

Fig.3

Schematic diagram of multiple feature extraction module, attention unit, and cross layer connections"

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