基于全局和局部特征信息的生成对抗网络在海马体分割中的应用

doi:10.11938/cjmr20243130

摘要/Abstract

摘要：

海马体由于结构复杂、体积小，导致对其进行精准分割较为困难．为此，本文提出一种基于全局和局部特征信息的生成对抗网络（GLGAN）分割方法．首先，为了提高网络稳定性和海马体分割精度，减少信息丢失和梯度爆炸等问题，本文通过改进生成对抗网络的生成器和损失函数，提出了全局生成对抗网络（GGAN）．其次，由于判别器本质上是二分类的分类器，对微小局部变换不敏感，于是提出具有全局和局部特征信息的双判别器网络结构的生成对抗网络．最后，设计一个平衡生成对抗网络（GAN）对抗性损失和3D u-net分割损失的总损失函数．实验结果表明基于GLGAN的分割方法有利于密集评估海马体，促进判别器将生成器生成的掩膜值推向更真实分布，提高海马体分割精度．该方法分割海马体的Dice系数为0.804、IOU为0.672．

关键词: 生成对抗网络(GAN), 3D卷积神经网络, 分割, 海马体, 3D u-net

Abstract:

Due to the complex structure and small size of the hippocampus, precise segmentation of the hippocampus remains challenging. To address this issue, this study proposes a generative adversarial network (GAN) based on global and local feature information (GLGAN) for hippocampus segmentation. First, to improve network stability and segmentation accuracy while reducing the likelihood of problems such as information loss and gradient explosion, we proposed the global GAN (GGAN) by optimizing the generator and loss function of GAN. Second, since the discriminator is essentially a binary classifier and is not sensitive to small local changes, we introduced a GAN method of dual discriminator network structure that integrates both global and local feature information. Finally, a total loss function was designed to balance GAN adversarial loss and 3D u-net segmentation loss. The experimental results show that proposed method based on GLGAN facilitates intensive evaluation of the hippocampus, and drives the discriminator to push the mask value provided by the generator to a more realistic distribution, thereby enhancing hippocampus segmentation accuracy. The Dice coefficient and IOU for hippocampus segmentation using GLGAN are 0.804 and 0.672 respectively.

Key words: generative adversarial network (GAN), 3D CNN, segmentation, hippocampus, 3D u-net

中图分类号:

O482.53

魏志宏, 孔旭东, 孔燕, 闫士举, 丁阳, 魏贤顶, 孔栋, 杨波. 基于全局和局部特征信息的生成对抗网络在海马体分割中的应用[J]. 波谱学杂志, 2025, 42(2): 143-153.

WEI Zhihong, KONG Xudong, KONG Yan, YAN Shiju, DING Yang, WEI Xianding, KONG Dong, YANG Bo. Application of Generative Adversarial Networks Based on Global and Local Feature Information in Hippocampus Segmentation[J]. Chinese Journal of Magnetic Resonance, 2025, 42(2): 143-153.

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类别	年龄	女性人数	男性人数	总人数
正常人	76.52±5.79	20	26	46
轻度认知障碍	75.24±7.13	13	48	61
阿尔茨海默病	75.24±6.35	13	12	25

类型	步长	卷积核	输出
Conv	2,2,2	5,5,5	64
Conv	2,2,2	5,5,5	128
Conv	2,2,2	5,5,5	256
Conv	2,2,2	5,5,5	512
Conv	2,2,2	5,5,5	512
Conv	2,2,2	5,5,5	512
FC	---	---	512

类型	步长	卷积核	输出
Conv	2,2,2	5,5,5	64
Conv	2,2,2	5,5,5	128
Conv	2,2,2	5,5,5	256
Conv	2,2,2	5,5,5	512
Conv	2,2,2	5,5,5	512
FC	---	---	512

类型	步长	卷积核	输出
Concatenate	---	---	1024
FC	---	---	1

方法	Dice	IOU
u-net^[17]	0.631	0.461
GGAN(u-net)	0.716	0.558
GGAN(v-net)	0.678	0.513
GLGAN（本文方法）	0.804	0.672