Synaptic partner prediction from point annotations in insect brains

• Main Authors: Buhmann, Julia, Krause, Renate, Lentini, Rodrigo Ceballos, Eckstein, Nils, Cook, Matthew, Turaga, Srinivas, Funke, Jan
• Format: Journal Article
• Language: English
• Published: 21-06-2018
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.1806.08205

High-throughput electron microscopy allows recording of lar- ge stacks of
neural tissue with sufficient resolution to extract the wiring diagram of the
underlying neural network. Current efforts to automate this process focus
mainly on the segmentation of neurons. However, in order to recover a wiring
diagram, synaptic partners need to be identi- fied as well. This is especially
challenging in insect brains like Drosophila melanogaster, where one
presynaptic site is associated with multiple post- synaptic elements. Here we
propose a 3D U-Net architecture to directly identify pairs of voxels that are
pre- and postsynaptic to each other. To that end, we formulate the problem of
synaptic partner identification as a classification problem on long-range edges
between voxels to encode both the presence of a synaptic pair and its
direction. This formulation allows us to directly learn from synaptic point
annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle
annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and
improve over the current state of the art, producing 3% fewer errors than the
next best method.