SkNeXt reduces cost of brain cell mapping from huge microscopy data
SkNeXt enables topology-guided neuronal reconstruction from petabyte-scale microscopy data
Computer Vision and Pattern Recognition
Summary
Mapping the shape and connections of brain cells from large, detailed microscope images is usually very slow and expensive. The authors developed SkNeXt, a new method that first summarizes brain cell shapes in simple outline forms to focus on the important parts. This lets them fix mistakes easily and only look at detailed images where needed, saving lots of time and computer work. Using SkNeXt, they mapped neurons from a very large mouse brain dataset using a single graphics processor in just one week.
What this means in practice
- •For neuroscience data teams: Quickly reconstruct neuron shapes and connections from extremely large brain imaging datasets using limited computing resources.
- •For medical image analysts: Focus detailed image processing just on relevant neuron paths to reduce processing time and storage when working with large microscopy scans.
Authors
Jiayi Ding, Hu Zhao
Abstract
Recent advances in high-resolution fluorescence and electron microscopy have enabled nanoscale imaging across increasingly large brain volumes, but the resulting terabyte- to petabyte-scale datasets make complete neuronal reconstruction prohibitively expensive in computation, data movement, and manual proofreading. Here, we present SkNeXt, a topology-first framework for scalable neuronal reconstruction from large volumetric microscopy datasets. Instead of densely processing entire image volumes, SkNeXt first converts neuronal morphology into compact SWC skeletons that preserve long-range connectivity. Proofreading is therefore focused on sparse neuronal trees, allowing branch, continuity, and connectivity errors to be corrected before high-resolution reconstruction. The corrected skeletons then serve as persistent structural priors for recovering detailed morphology while preserving neuronal identity and topology. Crucially, SkNeXt also uses neuronal skeletons as spatial indices for selective data access, retrieving high-resolution image regions only along reconstructed trajectories and bypassing most background and signal-free volumes. This substantially reduces I/O and computational overhead, allowing reconstruction cost to scale with neuronal morphology rather than total dataset size. Using SkNeXt, we reconstructed neurons from a petabyte-scale super-resolution fluorescence dataset of the mouse brain on a single GPU within one week, without requiring exhaustive dense inference across the complete imaging volume.