  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.
