MTDCNet: A 3D multi-threading dilated convolutional network for brain tumor automatic segmentation

被引:11
作者
Chen, Wankun [1 ]
Zhou, Weifeng [1 ]
Zhu, Ling [1 ]
Cao, Yuan [2 ]
Gu, Haiming [1 ]
Yu, Bin [2 ,3 ]
机构
[1] Qingdao Univ Sci & Technol, Coll Math & Phys, Qingdao 266061, Peoples R China
[2] Qingdao Univ Sci & Technol, Coll Informat Sci & Technol, Sch Data Sci, Qingdao 266061, Peoples R China
[3] Univ Sci & Technol China, Sch Data Sci, Hefei 230027, Peoples R China
基金
中国国家自然科学基金;
关键词
Dilated connect; Multi -threading dilated convolution; Spatial pyramid convolution; Multi -threading adaptive pooling strategy; Brain tumor segmentation;
D O I
10.1016/j.jbi.2022.104173
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Glioma is one of the most threatening tumors and the survival rate of the infected patient is low. The automatic segmentation of the tumors by reliable algorithms can reduce diagnosis time. In this paper, a novel 3D multithreading dilated convolutional network (MTDC-Net) is proposed for the automatic brain tumor segmentation. First of all, a multi-threading dilated convolution (MTDC) strategy is introduced in the encoder part, so that the low dimensional structural features can be extracted and integrated better. At the same time, the pyramid matrix fusion (PMF) algorithm is used to integrate the characteristic structural information better. Secondly, in order to make the better use of context semantical information, this paper proposed a spatial pyramid convolution (SPC) operation. By using convolution with different kernel sizes, the model can aggregate more semantic information. Finally, the multi-threading adaptive pooling up-sampling (MTAU) strategy is used to increase the weight of semantic information, and improve the recognition ability of the model. And a pixel-based post-processing method is used to prevent the effects of error prediction. On the brain tumors segmentation challenge 2018 (BraTS2018) public validation dataset, the dice scores of MTDC-Net are 0.832, 0.892 and 0.809 for core, whole and enhanced of the tumor, respectively. On the BraTS2020 public validation dataset, the dice scores of MTDCNet are 0.833, 0.896 and 0.797 for the core tumor, whole tumor and enhancing tumor, respectively. Mass numerical experiments show that MTDC-Net is a state-of-the-art network for automatic brain tumor segmentation.
引用
收藏
页数:13
相关论文
共 56 条
  • [1] Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture
    Anand, Vikas Kumar
    Grampurohit, Sanjeev
    Aurangabadkar, Pranav
    Kori, Avinash
    Khened, Mahendra
    Bhat, Raghavendra S.
    Krishnamurthi, Ganapathy
    [J]. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT II, 2021, 12659 : 310 - 319
  • [2] Bakas S, 2019, Arxiv, DOI [arXiv:1811.02629, 10.48550/arXiv.1811.02629, DOI 10.48550/ARXIV.1811.02629]
  • [3] Data Descriptor: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
    Bakas, Spyridon
    Akbari, Hamed
    Sotiras, Aristeidis
    Bilello, Michel
    Rozycki, Martin
    Kirby, Justin S.
    Freymann, John B.
    Farahani, Keyvan
    Davatzikos, Christos
    [J]. SCIENTIFIC DATA, 2017, 4
  • [4] Review of brain MRI image segmentation methods
    Balafar, M. A.
    Ramli, A. R.
    Saripan, M. I.
    Mashohor, S.
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2010, 33 (03) : 261 - 274
  • [5] Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy
    Ben Naceur, Mostefa
    Akil, Mohamed
    Saouli, Rachida
    Kachouri, Rostom
    [J]. MEDICAL IMAGE ANALYSIS, 2020, 63
  • [6] Chandra Satish, 2009, Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), P666, DOI 10.1109/NABIC.2009.5393455
  • [7] Brain tumor segmentation with deep convolutional symmetric neural network
    Chen, Hao
    Qin, Zhiguang
    Ding, Yi
    Tian, Lan
    Qin, Zhen
    [J]. NEUROCOMPUTING, 2020, 392 : 305 - 313
  • [8] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [9] Ciresan D., 2012, ADV NEURAL INFORM PR, P2843
  • [10] CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 20052009
    Dolecek, Therese A.
    Propp, Jennifer M.
    Stroup, Nancy E.
    Kruchko, Carol
    [J]. NEURO-ONCOLOGY, 2012, 14 : v1 - v49