653 research outputs found
Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model
Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns
Visualization and Analysis of 3D Microscopic Images
In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain
Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets
Hieroglyph: Hierarchical Glia Graph Skeletonization and Matching
Automatic 3D reconstruction of glia morphology is a powerful tool necessary
for investigating the role of microglia in neurological disorders in the
central nervous system. Current glia skeleton reconstruction techniques fail to
capture an accurate tracing of the processes over time, useful for the study of
the microglia motility and morphology in the brain during healthy and diseased
states. We propose Hieroglyph, a fully automatic temporal 3D skeleton
reconstruction algorithm for glia imaged via 3D multiphoton microscopy.
Hieroglyph yielded a 21% performance increase compared to state of the art
automatic skeleton reconstruction methods and outperforms the state of the art
in different measures of consistency on datasets of 3D images of microglia. The
results from this method provide a 3D graph and digital reconstruction of glia
useful for a myriad of morphological analyses that could impact studies in
brain immunology and disease.Comment: submitted to IEEE International Conference on Image Processing, 201
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy
3D Neuron Tip Detection in Volumetric Microscopy Images
Abstract-This paper addresses the problem of 3D neuron tips detection in volumetric microscopy image stacks. We focus particularly on neuron tracing applications, where the detected 3D tips could be used as the seeding points. Most of the existing neuron tracing methods require a good choice of seeding points. In this paper, we propose an automated neuron tips detection method for volumetric microscopy image stacks. Our method is based on first detecting 2D tips using curvature information and a ray-shooting intensity distribution model, and then extending it to the 3D stack by rejecting false positives. We tested this method based on the V3D platform, which can reconstruct a neuron based on automated searching of the optimal 'paths' connecting those detected 3D tips. The experiments demonstrate the effectiveness of the proposed method in building a fully automatic neuron tracing system
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