SNAP: Neuron Segmentation from Supervoxels and Pairwise Affinities

The ability to accurately reconstruct the architecture of neurons is a prerequisite for understanding the function of natural neural circuits. Existing neuron segmentation methods often rely on complex post-processing pipelines that require careful hyperparameter tuning. We introduce SNAP, a neuron segmentation method that jointly predicts supervoxels and their mutual affinities, reducing inference to a simple affinity-driven agglomeration procedure. On the established AC3/AC4 and FIB-25 benchmarks, our method achieves state-of-the-art results with inference time comparable to conventional affinity-based approaches. Experiments on these datasets demonstrate the robustness of our method across imaging modalities and dataset scales. Beyond segmentation performance, we revisit commonly used evaluation protocols and clarify inconsistencies in dataset splits and evaluation settings that complicate comparison between existing methods. Our results show that state-of-the-art neuron segmentation can be achieved without complex post-processing while providing a clearer and more reproducible basis for future comparisons.