Correction of topological errors in automated traces of neurites.

Our understanding of synaptic connectivity in the brain relies on the ability to accurately trace sparsely labeled neurons from 3D optical microscopy stacks of images. A variety of automated algorithms and software tools have been developed for this task. These algorithms can capture the general layout of neurites with high fidelity, but the resulting traces often contain topological errors such as broken and incorrectly merged branches. Even a small number of isolated topological errors can drastically alter the connectivity, and therefore, their detection and correction are paramount for connectomics studies. Here, we describe an automated trace proofreading approach that utilizes machine learning to correct trace topology. Multiple stacks of neuron images were traced by two users to create a labeled dataset and assess the baseline of inter-user variability. All traces were then disconnected at branch points and a deep neural network was trained to detect the correct way of reconnecting the branches. Custom morphological features were generated for each cluster of branch points, in a way that is dependent on a merging scenario but invariant to translations, rotations, and reflections of the cluster in the imaging plane. The features and image volume centered at the branch point were used for training a neural network that concatenates these input streams and outputs the confidence measure for different branch merging scenarios. The designed method significantly reduces the number of topological errors in automated traces and comes close to the accuracy achieved by expert users which is the gold standard in the field.