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DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentation methods are required for accurate and timely tre...

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Published in:Journal of Medical Systems Vol. 43; no. 7
Main Authors: Sun, Jiawei, Chen, Wei, Peng, Suting, Liu, Boqiang
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Jul2019
Online Access:View this record in EBSCOhost
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      jtl: Journal of Medical Systems
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      dt: Jul2019
      vid: 43
      iid: 7
      pid: 237
      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1358-6
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      tig:
        atl: DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.
      aug:
        au:
          Sun, Jiawei
          Chen, Wei
          Peng, Suting
          Liu, Boqiang
        affil: School of Control Science and Engineering, Shandong University, 250061, Jinan, China
      sug:
        subj:
          Brain Neoplasms Classification
          Deep Learning
          Neural Networks (Computer)
          Magnetic Resonance Imaging Methods
          Image Processing, Computer Assisted
          Education, Medical
          Human
          Quantitative Studies
          Autoanalysis
          Architecture
          Data Analysis, Computer Assisted
          Experimental Studies
          Ablation Techniques
      ab: Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentation methods are required for accurate and timely treatment. Recently, segmentation methods based on deep learning are popular because of their self-learning and generalization ability. Therefore, we propose a novel automatic 3D CNN-based method for brain tumor segmentation. In order to better capture the contextual information, we design the network architecture based on u-net and replace the simple skip connection with encoder adaptation blocks. To further improve the performance and reduce computational burden at the same time, we also use dense connected fusion blocks in decoder. We train our model with generalised dice loss function to alleviate the problem of class imbalance. The proposed model is evaluated on the BRATS 2015 testing dataset and obtains dice scores of 0.84, 0.72 and 0.62 for whole tumor, tumor core and enhancing tumor, respectively. Our model is accurate and efficient, achieving results that comparable to the reported state-of-the-art results.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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