Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction

TL;DR

This paper introduces ViterBrain, a probabilistic hidden Markov model-based method for automated neuron reconstruction in brain images, capable of handling noise, multiple neurons, and imperfect segmentations.

Contribution

It presents a novel global maximization approach using hidden Markov models combined with computer vision techniques for neuron reconstruction.

Findings

Successfully reconstructs neurons in noisy, multi-neuron images

Operates effectively on imperfect image segmentations

Provides an open-source Python package for the method

Abstract

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 neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron fluorescence. Our method utilizes dynamic programming to compute the global maximizers of what we call the "most probable" neuron path. Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons. Our method operates on image segmentations in order to leverage cutting edge computer vision technology. We applied our algorithm to imperfect image segmentations where false negatives severed neuronal processes, and showed that it can follow axons in the presence of noise or nearby neurons. Additionally, it creates a framework where users can intervene to, for example, fit start and endpoints. The code used in this work is available in our open-source Python package brainlit.