ARGA-Unet:利用残差分组卷积和注意力机制进行脑肿瘤 MRI 图像分割的高级 U 网分割模型

Q1 Computer Science
Siyi XUN , Yan ZHANG , Sixu DUAN , Mingwei WANG , Jiangang CHEN , Tong TONG , Qinquan GAO , Chantong LAM , Menghan HU , Tao TAN
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引用次数: 0

摘要

背景磁共振成像(MRI)在医学影像诊断技术的快速发展中发挥了重要作用,尤其是在脑肿瘤的诊断和治疗方面,因为它具有无创的特点和卓越的软组织对比度。然而,脑肿瘤由于其侵袭性和高度异质性,在核磁共振成像图像中具有高度不均匀和边界不明显的特点。为了解决这些问题,本研究使用残差分组卷积模块、卷积块注意模块和双线性插值上采样方法来改进经典的分割网络 U-net。结果在实验中,所提出的分割模型的 Dice 分数达到了 97.581%,比传统 U-net 高出 12.438%,证明了对核磁共振脑肿瘤图像的有效分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ARGA-Unet: Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background

Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious.

Methods

To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered.

Results

In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images.

Conclusions

In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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