Recut: a Concurrent Framework for Sparse Reconstruction of Neuronal Morphology

Abstract

Advancement in modern neuroscience is bottlenecked by neural reconstruction, a process that extracts 3D neuron morphology (typically in tree structures) from image volumes at the scale of hundreds of GBs. We introduce Recut, an automated and accelerated neural reconstruction pipeline, which provides a unified, and domain specific sparse data representation with 79x reduction in the memory footprint. Recuts reconstruction can process 111 Kneurons/day or 79 TB/day on a 24-core workstation, placing the throughput bottleneck back on microscopic imaging time. Recut allows the full brain of a mouse to be processed in memory on a single server, at 89.5x higher throughput over existing I/O-bounded methods. Recut is also the first fully parallelized end-to-end automated reconstruction pipeline for light microscopy, yielding tree morphologies closer to ground truth than the state-of-the-art while removing involved manual steps and disk I/O overheads. We also optimized pipeline stages to linear algorithmic complexity for scalability in dense settings and allow the most timing-critical stages to optionally run on accelerated hardware.