Abstract
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.
Highlights
Our understanding of brain functions is hindered by the lack of detailed knowledge of synaptic connectivity in the underlying neural network
The OP dataset includes 9 image stacks containing axons of single olfactory projection neurons from Drosophila (Jefferis et al, 2007), and the L6 dataset consists of 6 image stacks containing axons of multiple layer 6 neurons imaged in layer 1 of mouse visual cortex (De Paola et al, 2006)
The initial trace is dismantled to the level of individual branches, and active learning is applied to reconnect this trace based on knowledge of neuron morphology
Summary
Our understanding of brain functions is hindered by the lack of detailed knowledge of synaptic connectivity in the underlying neural network. Imaging can be done in vivo for circuit development or plasticity studies (Trachtenberg et al, 2002), or ex vivo for circuit mapping projects (Lu et al, 2009). In the latter case, an unprecedented resolution can be achieved by first clarifying the tissue (Hama et al, 2011; Chung et al, 2013), and imaging the entire brain from thousands of optical sections (Ragan et al, 2012). Accurate traces of complex neuron morphologies can only be obtained manually, which is extremely time consuming (Stepanyants et al, 2004, 2008; Shepherd et al, 2005), and impractical for large reconstruction projects
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