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The size of image stacks in connectomics studies now reaches the terabyte andoften petabyte scales with a great diversity of appearance across brain regionsand samples. However, manual annotation of neural structures, e.g., synapses,is time-consuming, which leads to limited training data often smaller than0.001\% of the test data in size. Domain adaptation and generalizationapproaches were proposed to address similar issues for natural images, whichwere less evaluated on connectomics data due to a lack of out-of-domainbenchmarks.

An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes