Abstract
BackgroundNeurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files.ResultsWe propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise.ConclusionsThis method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.
Highlights
Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification
The first is provided by Wang et al [11], and it consists of genetic labeling chandelier cells imaged by the Fluorescence micro-optical sectioning tomography (fMOST) system with a resolution of 0.2 × 0.2 × 1 μm3
The second is provided by Zhang et al [23] and Jiang et al [24], and it consists of pyramidal neurons at the same resolution
Summary
Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The nodes are marked around the centerline of the neuronal fiber, not on the centerline These inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. Neuron tracing is an important technique in neuroscience that includes many automatic tracing methods, such as skeletonization [12, 13], minimum spanning trees [14], snake models [15, 16], principle curve models [17] and neural network methods [18] These automatic tracing methods are always used in small image blocks and work well. When dealing with a whole-brain image dataset, researchers prefer semimanual tracing [2, 8, 19,20,21] (researchers manually mark the neuron fiber nodes according the images, and software automatically links these nodes to create a neuron morphology file)
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