  This paper describes a scalable active learning pipeline prototype for
large-scale brain mapping that leverages high performance computing power. It
enables high-throughput evaluation of algorithm results, which, after human
review, are used for iterative machine learning model training. Image
processing and machine learning are performed in a batch layer. Benchmark
testing of image processing using pMATLAB shows that a 100$\times$ increase in
throughput (10,000%) can be achieved while total processing time only increases
by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust
scalability. The images and algorithm results are provided through a serving
layer to a browser-based user interface for interactive review. This pipeline
has the potential to greatly reduce the manual annotation burden and improve
the overall performance of machine learning-based brain mapping.
