Deep learning-based TB-level cranial nerve fiber data reduction method and system

A technology of nerve fiber and deep learning, applied in the field of image processing, to achieve the effect of ensuring robustness, simple algorithm, and wide application range

Active Publication Date: 2021-08-27
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The generation of massive data and the hysteresis of data processing tools pose new challenges to the reconstruction of neuron morphology

Method used

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  • Deep learning-based TB-level cranial nerve fiber data reduction method and system
  • Deep learning-based TB-level cranial nerve fiber data reduction method and system
  • Deep learning-based TB-level cranial nerve fiber data reduction method and system

Examples

Experimental program
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Effect test

Embodiment 1

[0045] In the present embodiment, the method for reducing the whole brain data of mice with TB level sparsity comprises the following steps:

[0046] S1: Obtain the image to be tested and construct a whole brain test data set.

[0047] For example, the original image is selected from a mouse brain slice image obtained by a fluorescence microsection imaging system or a functional two-photon confocal imaging microscope. The test image data set is from the coronal slice data of the whole brain, and every 200 layers of coronal slices are used for maximum projection (the selection of the number of layers directly determines the size of the data volume of the data set to be tested; of course, in addition to 200 layers, there are also The specific number of layers can be adjusted according to the actual situation; when adjusting the number of layers, the quality of the projection data should be considered to avoid: i. the number of layers is too small, and the nerve fiber information...

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Abstract

The invention belongs to the field of image processing, and discloses a deep learning-based TB magnitude 3D cranial nerve data reduction method and system, and the method comprises the following steps: S1, building a test data set; s2, establishing a segmentation model and training the segmentation model; s3, processing each to-be-tested image in the test data set by using the segmentation model to obtain nerve fiber distribution information corresponding to each to-be-tested image; and S4, performing optimization processing on the nerve fiber distribution information by using morphological operation, so that the maximum connected domain corresponding to the region where the nerve fibers are located in the nerve fiber profile map under the spatial three-dimensional dimension is optimized, and finally obtaining a TB-level sparse whole-brain nerve fiber data reduction result. The invention can rapidly, accurately and effectively reduce the TB-magnitude and above cranial nerve data set, greatly reduces the data volume of subsequent neuron reconstruction, and improves the reconstruction efficiency.

Description

technical field [0001] The present invention belongs to the field of image processing, and more specifically relates to a method and system for reducing TB-level brain nerve fiber data based on deep learning. The method and system for reducing TB-level sparse brain nerve fiber data based on deep learning can be used for the reduction of whole-brain nerve fiber data in biomedical images. Background technique [0002] In recent years, a series of breakthroughs in molecular labeling and imaging technologies have made whole-brain-scale neural community imaging with single-cell resolution a reality. The generation of massive data and the lag of data processing tools pose new challenges to the reconstruction of neuron morphology. Neurons are widely distributed in the whole brain, and the distribution of each brain region is different, and there is a characteristic of sparseness on the scale of the whole brain, which makes it possible to reduce the nerve fibers in the whole brain....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10G06T7/13G06T5/30G06N3/04
CPCG06T7/0012G06T7/10G06T7/13G06T5/30G06T2207/20081G06T2207/30016G06T2207/10012G06N3/045
Inventor 全廷伟黄青刘世杰骆清铭曾绍群
Owner HUAZHONG UNIV OF SCI & TECH
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