Planar to Spatial: a Synapse Reconstruction Method to Rebuild Voxel Connections for Anisotropic Serial EM Images.

Reconstructing synapses from anisotropic serial electron microscopy (EM) images remains challenging, especially in restoring neural circuits. Recently, hybrid 2D/3D convolution is proposed to tackle this anisotropy problem. However, Z-axis anisotropy results in distinct structural changes and voxel-level shifts. As a result, the vanilla convolution is ineffective for recovering the instance details end-to-end from anisotropic semantic information, resulting in split/merge or deviation errors. In this study, we propose a synapse reconstruction method to rebuild spatial connections of voxels from accurate planar semantics. First, we design a region-focused Mask R-CNN with a cross-patch fusion method. Anchors and losses are constrained by instance boxes to accommodate the narrow structure of synapses. Furthermore, instance confidences are used for multi-scenario duplicate detection fusion. Second, we present a novel synaptic similarity metric on the basis of synaptic shape and spatial information. A learnable hierarchical clustering strategy based on this metric is performed to obtain the voxel connections. Experiments on anisotropic EM data show the superior performance of our method, especially on the integrity of reconstructed structures. Our code is available at https://github.com/fenglingbai/SynapseReconstruction.