The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples. However, manual annotation of neural structures, e.g., synapses, is time-consuming, which leads to limited training data often smaller than 0.001\% of the test data in size. Domain adaptation and generalization approaches were proposed to address similar issues for natural images, which were less evaluated on connectomics data due to a lack of out-of-domain benchmarks.

翻译：连接组学研究中的图像堆栈规模现已达到太字节甚至拍字节量级，且不同脑区与样本间的外观呈现高度多样性。然而，神经结构（如突触）的人工标注耗时巨大，导致训练数据规模往往不足测试数据的0.001%。尽管针对自然图像中相似问题已提出域适应与泛化方法，但由于缺乏域外基准评估平台，这些方法在连接组学数据上的有效性尚未得到充分验证。