Neuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells-bottlenecks the study of brain function, connectomics and pathology. Unlike artistic domains with similar challenges (e.g., hair modeling), scientists need exact and complete segmentations to study subtle topological differences. Existing methods are disk-bound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons' shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting vision tasks such as segmentation and skeletonization into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67× the scale with 870× less memory while having 78% higher automated yield than the highest performing alternative methods.