Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction

Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of projection neuron morphology, but manual neuron reconstruction remains a bottleneck. Here we present a method inspired by hidden Markov modeling and appearance modeling of fluorescent neuron images that can automatically trace neuronal processes. Our method leverages dynamic programming to scale to terabyte sized image data and can be applied to images with one or more neurons. We applied our algorithm to the output of image segmentation models where false negatives severed neuronal processes, and showed that it can follow axons in the presence of noise or nearby neurons. Our method has the potential to be integrated into a semi or fully automated reconstruction pipeline. Additionally, it creates a framework through which users can intervene with hard constraints to, for example, rule out certain reconstructions, or assign axons to particular cell bodies.

AutoGMM: Automatic Gaussian Mixture Modeling in Python

Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation. However, estimating the optimal model for any given number of components is an NP-hard problem, and estimating the number of components is in some respects an even harder problem. In R, a popular package called mclust addresses both of these problems. However, Python has lacked such a package. We therefore introduce AutoGMM, a Python algorithm for automatic Gaussian mixture modeling. AutoGMM builds upon scikit-learn's AgglomerativeClustering and GaussianMixture classes, with certain modifications to make the results more stable. Empirically, on several different applications, AutoGMM performs approximately as well as mclust. This algorithm is freely available and therefore further shrinks the gap between functionality of R and Python for data science.