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.

Active learning of neuron morphology for accurate automated tracing of neurites.

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.