基于生成对抗网络的海马子区图像分割

海马子区体积很小且结构复杂，传统分割方法无法达到理想分割效果，为此引入生成对抗网络模型用于海马子区图像分割.该方法构建一个生成对抗网络模型，通过构建生成网络和对抗网络并对其进行交替对抗训练实现对脑部海马子区图像的像素级精确分割.实验选取美国旧金山CIND中心的32位实验者的脑部MRI图像进行海马子区分割测试，在定性和定量方面分别对比了所提方法基于稀疏表示与字典学习方法和传统CNN的分割结果.实验结果表明，该方法优于基于稀疏表示与字典学习和CNN方法，海马子区分割准确率有较大提升.该方法提升了海马子区的分割准确率，可用于大脑核磁图像中海马子区的分割，为诸多神经退行性疾病的临床诊断与治疗提供依据.

Image Segmentation of Hippocampal Subfields with Generative Adversarial Networks

Attribute to the small structures and the morphological complexity of the hippocampal subfields, it is hard to obtain desirable segmentation results with the traditional segmentation methods. Therefore,we introduce generative adversarial networks into image segmentation of hippocampal subfields. The introduced method can achieve the pixel-level segmentation of brain MR images. The generative model and the adversarial model are trained alternately. The approach was tested based on the brain MRI images of 32 volunteers from the CIND Center in San Francisco, USA. It was compared quantitatively and qualitatively with methods based on the sparse representation and dictionary learning and CNN. The results showed that the proposed method, which achieved a significant improvement in the segmentation accuracy of the hippocampal subfields, outperforms the existing methods based on the dictionary learning and sparse representation and CNN. The results reveal that the introduced method can effectively improve the segmentation accuracy of hippocampal subfields in the brain MRI images, which will provide the basis for the clinical diagnosis and treatment of neurodegenerative diseases.