Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 12-16.doi: 10.11896/jsjkx.210700217

• Smart Healthcare • Previous Articles     Next Articles

Brain Tumor Segmentation Algorithm Based on Multi-scale Features

SUN Fu-quan1, CUI Zhi-qing1,2, ZOU Peng1,2, ZHANG Kun1   

  1. 1 School of Mathematics and Statistics,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066000,China
    2 School of Information Science and Engineering,Northeastern University,Shenyang 110000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:SUN Fu-quan,born in 1964,Ph.D,postdoctoral fellow,professor.His main research interests include medical image processing and big data analysis.
    CUI Zhi-qing,born in 1997,postgra-duate.Her main research interests include image processing and computer vision.
  • Supported by:
    National Key R & D Program of China(2018YFB1402800) and Hebei Higher Education Research Practice Project(2018GJJG422).

Abstract: Brain tumors are the most common diseases of nervous system except cerebrovascular disease,and their segmentation is also an important field in medical image processing.Accurately segmenting the tumor region is the first step in the treatment of brain tumors.Aiming at the problem of information loss caused by the weak multi-scale processing ability of traditional fully convolutional networks,a fully convolutional network based on multi-scale features is proposed.Using spatial pyramid pooling to obtain advanced features of multiple receptive fields,thereby capturing contextual multi-scale information and improving the adaptability to different scale features.Replacing the original convolution layer with the residual compact module can alleviate the degradation problem and extract more features.The data augmentation technology is combined to enhance the segmentation perfor-mance maximally while avoiding over fitting.Through a large number of contrastive ablation experiments on the public low-grade glioma MRI dataset,using Dice coefficient,Jaccard index and accuracy as the main evaluation criteria,91.8% Dice coefficient,85.0% Jaccard index and 99.5% accuracy are obtained.Experimental results show that the proposed method can effectively segment brain tumor regions and have certain generalization,and the segmentation effect is better than other networks.

Key words: Brain tumor segmentation, Deep learning, Fully convolutional network, Magnetic resonance imaging, Multi-scale features

CLC Number: 

  • TP391
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