Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI
文献类型: 会议论文
第一作者: Tongxue Zhou
作者: Tongxue Zhou 1 ; Su Ruan 2 ; Haigen Hu 2 ; Stephane Canu 3 ;
作者机构: 1.Universite de Rouen Normandie, LITIS - QuantIF, 76183 Rouen, France,INSA Rouen, LITIS - Apprentissage, 76800 Rouen, France
2.Universite de Rouen Normandie, LITIS - QuantIF, 76183 Rouen, France,College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
3.Normandie Univ, INSA Rouen, UNIROUEN, UNIHAVRE, LITIS, Rouen, France
关键词: Deep learning;Brain tumor segmentation;Residual block;Deep supervision
会议名称: International Workshop on Machine Learning in Medical Imaging
主办单位:
页码: 574-582
摘要: Automatic segmentation of brain tumor in magnetic resonance images (MRI) is necessary for diagnosis, monitoring and treatment. Manual segmentation is time-consuming, expensive and subjective. In this paper we present a robust automatic segmentation algorithm based on 3D U-Net. We propose a novel residual block with dilated convolution (res.dil block) and incorporate deep supervision to improve the segmentation results. We also compare the effect of different losses on the class imbalance problem. To prove the effectiveness of our method, we analyze each component proposed in the network architecture and we demonstrate that segmentation results can be improved by these components. Experiment results on the BraTS 2017 and BraTS 2018 datasets show that the proposed method can achieve good performance on brain tumor segmentation.
分类号: tp3
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3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
作者:Tongxue Zhou;Stephane Canu;Pierre Vera;Su Ruan
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