An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes
This work tackles the problem of domain adaptation for connectomics researchers, but it appears incremental as it focuses on creating a benchmark rather than a novel method.
The paper addresses the challenge of limited training data for synapse detection in large-scale connectomics by introducing an out-of-domain benchmark, highlighting that manual annotation is time-consuming and training data is often less than 0.001% of test data.
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.