AI-enhanced segmentation of neuronal boundaries in electron microscopy (EM) images is crucial for automatic and accurate neuroinformatics studies. To enhance the limited generalization ability of typical deep learning frameworks for medical image analysis, unsupervised domain adaptation (UDA) methods have been applied. In this work, we propose to improve the performance of UDA methods on cross-domain neuron membrane segmentation in EM images. First, we designed a feature weight module considering the structural features during adaptation. Second, we introduced a structural feature-based super-resolution approach to alleviating the domain gap by adjusting the cross-domain image resolutions. Third, we proposed an orthogonal decomposition module to facilitate the extraction of domain-invariant features. Extensive experiments on two domain adaptive membrane segmentation applications have indicated the effectiveness of our method.

翻译：在电子显微镜图像中，基于人工智能增强的神经元边界分割对于自动且精确的神经信息学研究至关重要。为提升典型深度学习框架在医学图像分析中有限的泛化能力，无监督域适应（UDA）方法已被应用。本研究旨在改进UDA方法在电子显微镜跨域神经元膜分割任务中的性能。首先，我们设计了一个考虑结构特征的自适应特征权重模块；其次，引入了一种基于结构特征的超分辨率方法，通过调整跨域图像分辨率来缓解域差异；第三，提出了一个正交分解模块以促进域不变特征的提取。在两个域自适应膜分割应用上的大量实验表明了我们方法的有效性。