Hang Zhou

NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites

The reconstruction of neuronal populations, a key step in understanding neural circuits, remains a challenge in the presence of densely packed neurites. Here we achieved automatic reconstruction of neuronal populations by partially mimicking human strategies to separate individual neurons. For populations not resolvable by other methods, we obtained recall and precision rates of approximately 80%. We also demonstrate the reconstruction of 960 neurons within 3 h.

NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model

Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.

SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images

Digital reconstruction of a single neuron occupies an important position in computational neuroscience. Although many novel methods have been proposed, recent advances in molecular labeling and imaging systems allow for the production of large and complicated neuronal datasets, which pose many challenges for neuron reconstruction, especially when discontinuous neuronal morphology appears in a strong noise environment. Here, we develop a new pipeline to address this challenge. Our pipeline is based on two methods, one is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i.e., avoid the whole image analysis, and thus boosts the efficacy of computation; the other is constrained principal curves method for completing the neurite reconstruction, which uses the past reconstruction information of a neurite for current reconstruction and thus can be suitable for tracing discontinuous neurites. We investigate the reconstruction performances of our pipeline and some of the best state-of-the-art algorithms on the experimental datasets, indicating the superiority of our method in reconstructing sparsely distributed neurons with discontinuous neuronal morphologies in noisy environment. We show the strong ability of our pipeline in dealing with the large-scale image dataset. We validate the effectiveness in dealing with various kinds of image stacks including those from the DIADEM challenge and BigNeuron project.

Reconstruction of micron resolution mouse brain surface from large-scale imaging dataset using resampling-based variational model.

Brain surface profile is essential for brain studies, including registration, segmentation of brain structure and drawing neuronal circuits. Recent advances in high-throughput imaging techniques enable imaging whole mouse brain at micron spatial resolution and provide a basis for more fine quantitative studies in neuroscience. However, reconstructing micron resolution brain surface from newly produced neuronal dataset still faces challenges. Most current methods apply global analysis, which are neither applicable to a large imaging dataset nor to a brain surface with an inhomogeneous signal intensity. Here, we proposed a resampling-based variational model for this purpose. In this model, the movement directions of the initial boundary elements are fixed, the final positions of the initial boundary elements that form the brain surface are determined by the local signal intensity. These features assure an effective reconstruction of the brain surface from a new brain dataset. Compared with conventional typical methods, such as level set based method and active contour method, our method significantly increases the recall and precision rates above 97% and is approximately hundreds-fold faster. We demonstrated a fast reconstruction at micron level of the whole brain surface from a large dataset of hundreds of GB in size within 6 hours.

Advanced NeuroGPS-Tree achieves brain-wide reconstruction of neuronal population equal to manual reconstruction level (Conference Presentation)

The brain-wide reconstruction of neuronal population is an indispensible step towards exploring the complete structure of neuronal circuits, a central task that underlies the structure-function relation in neuroscience. Recent advances in molecular labeling and imaging techniques enable us to collect the whole mouse brain imaging dataset at cellular resolution, including the morphological information of neurons across different brain region or even the whole brain. Reconstruction of these neurons poses substantial challenges, and at presents there is no tool for high-speed achieving this reconstruction close to human performance. Here, we presented a tool for filling in the blanks. The tool mainly contains the following function modules: 3D visualization of large-scale imaging dataset, automated reconstruction of neurons, manual editing of the reconstructions at local and global scale. In this tool, in the framework of our previous tools (NeuroGPS-Tree and SparseTracer), the two identifying models were constructed for boosting the automatic level of the reconstruction. One is used to identify the weak signals from inhomogeneous backgrounds and the other is used to identify closely packed neurites. This tool can be suitable for the different big-data formats and can make the dataset be fastly read into memory for the reconstruction. The manual editing module in this tool can correct the errors drawn from above automated algorithms. And thus helps to achieve the reconstruction closer to human performance. We demonstrated the features of our tool on various kinds of sparsely labelled datasets. The results indicated that without loss of the reconstruction accuracy, our tool has a 7-10 folds speed gain over the commercial software that provides the manual reconstruction.

Identifying weak signals in inhomogeneous neuronal images for large-scale tracing of neurites

Reconstructing neuronal morphology across different regions or even the whole brain is important in many areas of neuroscience research. Large-scale tracing of neurites constitutes the core of this type of reconstruction and has many challenges. One key challenge is how to identify a weak signal from an inhomogeneous background. Here, we addressed this problem by constructing an identification model. In this model, empirical observations made from neuronal images are summarized into rules, which are used to design feature vectors that display the differences between the foreground and background, and a support vector machine is used to learn these feature vectors. We embedded this identification model into a tool that we previously developed, SparseTracer, and termed this integration SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace neurites with extremely weak signals (signal-to-background-noise ratio <1.1) against an inhomogeneous background. By testing 12 sub-blocks extracted from a whole imaging dataset, ST-LFV can achieve an average recall rate of 0.99 and precision rate of 0.97, which is superior to that of SparseTracer (which has an average recall rate of 0.93 and average precision rate of 0.86), indicating that this method is well suited to weak signal identification. We applied ST-LFV to trace neurites from large-scale images (approximately 105 GB). During the tracing process, obtaining results equivalent to the ground truth required only one round of manual editing for ST-LFV compared to 20 rounds of manual editing for SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the scale of an entire brain.

Advanced NeuroGPS-Tree: dense reconstruction of brain-wide neuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.