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

Abstract

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.

SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images

“…1) Localize the soma with NeuroGPS (Quan et al 2013), and label the localized soma's region with VM_SCS (Quan et al 2014). 2) Detect the initial part of the neurites that link with the current labeled region.…”

“…The detailed background estimation procedure can be found in our previous works (Quan et al 2013). After labeling the first layer and regard it as the current region, we use the above procedure on the first layer and can obtain the second layer, denoted by C 2 .…”

“…1 Examples of sparsely distributed neurons in strong noise environment: (a) a single neuron from the BigNeuron dataset; (b) enlargement of the apical dendrite; (c) enlargement of a neurite with weak signal, labeled by white arrow; (d) the foreground and background intensities of the highlight neurite (green line) shown in (b), the green line depict the skeleton of the neurite which composed by a series of manually determined centerline points. The intensity values of these points are calculated from the original image stack and estimated background image stack (Quan et al 2013). In some cases, the difference between these two intensities is small, labeled with a dashed square…”

Plastic embedding immunolabeled large-volume samples for three-dimensional high-resolution imaging

“…An accurate mechanical positioning stage (X-axis: ABL20020, Y-axis: ANT130, Z-axis: AVL125, Aerotech) was used to ensure the natural and accurate registration of all the images. 3D reconstruction of the images was implemented using previously reported methods [28][29][30][31][32]. Fig.…”

BioImage Informatics: The challenge of knowledge extraction from biological images

“…Furthermore, the microresolution combined with the relative large dimensions of the biological specimens produce terabyte-sized multidimensional image datasets, thus demanding for automated tools to extract quantitate information. Although a recent work still manually identifies dendritic spine in rat cerebellum acquired at the laser confocal microscope, thus being prone to oversight human error and limited to not so large 3D volume [18], there are recent efforts to deploy automatic tools for automatic image analysis that are, however, limited to relatively small volumes [19], [20], [21], [22], [23]. Moreover, in the literature few works deal with terabytes-sized datasets [3], [14], [24]: they reveal that not only tools for large scale image processing are required, but there is a compelling need of a new neuroinformatics framework for low and high level data analysis, allowing to detect statistical regularities and the relationships between data at different levels of brain organization.…”