Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking

The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative backtracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

Reconstruction of 3D neuron morphology using Rivulet back-tracking

The 3D reconstruction of neuronal morphology is a powerful technique for investigating nervous systems. Due to the noises in optical microscopic images, the automated reconstruction of neuronal morphology has been a challenging problem. We propose a novel automatic neuron reconstruction algorithm, Rivulet, to target the challenges raised by the poor quality of the optical microscopic images. After the neuron images being de-noised with an anisotropic filter, the Rivulet algorithm combines multi-stencils fast-marching and iterative back-tracking from the geodesic farthest point on the segmented foreground. The neuron segments are dumped or merged according to a set of criteria at the end of each iteration. The proposed Rivulet tracing algorithm is evaluated with data provided from the BigNeuron Project. The experimental results demonstrate that Rivulet outperforms the compared state-of-the-art tracing methods when the images are of poor quality

Gotta trace ‘em all: A mini-review on tools and procedures for segmenting single neurons toward deciphering the structural connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods