基于深度卷积神经网络的脑肿瘤MRI图像自动分割方法
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天津理工大学

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TP181

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国家重点研发计划项目(2022YFE0112500);国家自然科学(61873188);


Automated Segmentation of Brain Tumor MR Images Based on a Deep Convolutional Neural Network
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Tianjin University of Technology

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    摘要:

    脑肿瘤分割是脑肿瘤患者的诊断、治疗计划和监测的重要步骤,高效、准确地分割肿瘤可以大大提高医疗诊断的准确性和可靠性。本文设计了一种深度卷积神经网络模型实现对脑肿瘤MRI图像的精准自动分割,采用残差转残差网络鼓励特性重用,引入空洞卷积池化金字塔模块获取多尺度信息,加入空间与通道注意力机制高效提取特征,从而提高网络的鲁棒性,实现了高精度的脑肿瘤MRI图像自动分割。采用联合损失在个BRATS2021数据集上进行训练和测试,得到WT、TC和ET这三个分割区域内的不同评价指标结果,并与U-Net、Res-Unet、Dense-Unet、DeepLabv3+四种网络模型进行对比实验,证明了本文网络模型具有较强的分割性能,可以实现对脑肿瘤MRI图像的精准分割。

    Abstract:

    Brain tumor segmentation is an important step in the diagnosis, treatment planning, and monitoring of patients with brain tumors. Efficient and accurate segmentation of tumors can greatly improve the accuracy and reliability of medical diagnosis. In this paper, a deep convolutional neural network model is designed to achieve precise automatic segmentation of brain tumor MR images. Residual-to-residual networks are used to encourage feature reuse, and an atrous convolutional pyramid pooling module is introduced to obtain multiscale information. Spatial and channel attention mechanisms are used to efficiently extract features, thereby improving the robustness of the network and achieving high-precision automatic segmentation of brain tumor MRI images. The combined loss was trained and tested on BRATS2021 data sets, and the results of different evaluation indexes in WT, TC and ET were compared with U-Net, Res-Unet, Dense-Unet and DeepLabv3 +, which proved that the network model has strong segmentation performance and can realize accurate segmentation of brain tumor MR images.

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郭宇,郑来旺,刘振忠. 基于深度卷积神经网络的脑肿瘤MRI图像自动分割方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-06-09
  • 最后修改日期:2023-06-14
  • 录用日期:2023-06-15
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