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多尺度分割和误差引导生成对抗网络的神经元分割方法

Multi-scale Segmentation- and Error-guided Generative Adversarial Network for Neuron Segmentation

  • 摘要: 神经元精确实例分割是脑科学的核心问题之一, 是定量分析神经元数量、形态、分布等信息的关键. 准确的神经元语义分割算法可以提供精确的前景神经元像素, 进一步提高神经元实例分割的精度. 然而, 大脑结构复杂, 神经元类内差异大、类间差异小、高度黏连、在不同解剖区域分布不均匀的特征, 使得现有语义分割方法在应用到实际数据中时产生新的问题, 导致全脑神经元语义分割的精度受限. 本文提出一种多尺度分割和误差引导生成对抗网络(MSEG-GAN)的神经元分割方法. 该方法应用基于双注意力门的多尺度分割和误差引导的框架作为生成器, 预测神经元分割结果、假阴错误和假阳错误, 根据三者结果通过迭代的方式改善神经元语义分割结果. 然后, 将生成的分割结果、真值图分别与原始图像相乘, 作为判别器的两个输入, 通过多层卷积操作计算二者差异, 区分生成的分割结果和真值图. 在猕猴大脑数据集上, 将本文方法与7种典型方法(U-Net, AttU-Net, UNet++, MSEG-iCNN, SSAN, SegAN, SETR)进行了比较. 与其他方法相比, 本文方法得到的全脑神经元分割性能指标Dice值分别提高了2.8%、0.5%、2.5%、1.3%、16.8%、1.4%、10.1%, 尤其对于浅色神经元, 其分割性能得到有效提高, Dice值分别提高了20.3%、1.2%、15.5%、8.2%、159.2%、0.8%、13.1%. 本文所提出的多尺度分割和误差引导生成对抗网络的神经元分割方法, 可以精确分割全脑神经元, 可以进一步用于神经元的实例分割、形态学分析等研究.

     

    Abstract: Accurate instance segmentation of neurons is one of the core issues in brain science, and is the key to quantitatively analyzing information such as the number, morphology, and distribution of brain neurons. Accurate neuron semantic segmentation can provide accurate foreground neuron pixels, further improving the accuracy of neuron instance segmentation. However, the structure of the brain is complex, the intra-class differences among neurons is large, the inter-class differences among neurons is small, the neurons in certain anatomical regions are highly aggregated, and the distribution of neurons is uneven in different anatomical regions, causing new problems when existing semantic segmentation methods are applied to actual data, resulting in limited accuracy of semantic segmentation of neurons throughout the brain. In this paper, a multiscale segmentation- and error-guided generative adversarial network (MSEG-GAN) is proposed for neuron segmentation. This method applies the dual attention gate-based multiscale segmentation- and error-guided framework as the generator to predict neuron segmentation results, false negative errors and false positive errors. According to the three prediction results, the neuron semantic segmentation results are iteratively improved. Then, the generated segmentation results and the ground truth are, respectively, multiplied by the original images as the two inputs of the discriminator. The difference between the two branches is calculated by applying multiple convolutional operations to distinguish the generated segmentation results and the ground truth. The semantic segmentation results obtained by the proposed model in the macaque brain dataset were compared with seven reference methods (U-Net, AttU-Net, UNet++, MSEG-iCNN, SSAN, SegAN, SETR). Compared with the reference methods, the value of criterion Dice of the semantic segmentation performance of the whole brain neurons was, respectively, increased by 2.8%、0.5%、2.5%、1.3%、16.8%、1.4%、10.1%. Especially for light-stained neurons, the segmentation performance was effectively improved, and the Dice value was, respectively, increased by 20.3%、1.2%、15.5%、8.2%、159.2%、0.8%、13.1%. The proposed multiscale segmentation- and error-guided generative adversarial network can automatically and accurately segment the whole brain neurons, and can be further used for neuron instance segmentation and neuron morphological analysis.

     

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