White matter fiber tract reconstruction method based on deep learning

Abstract

The invention discloses a white matter fiber tract reconstruction method based on deep learning, which mainly aims at white matter fiber tract reconstruction. The method comprises the following steps: extracting signal sparsity features of a digital image in a training sample set; importing the signal sparsity features to a convolutional neural network for training, forwardly propagating the classification result, using a back propagation algorithm for the classification error, and getting a best network model; inputting extracted signal sparsity features of an image in a test sample set to a trained network model to get a final prediction result; and finally, describing the result into the orientation and distribution of white matter fibers through continuous curve fitting, and reconstructing a three-dimensional white matter fiber tract. Cross and forked white matter fiber tracts can be constructed accurately to provide help for the clinical research and physiological and pathological mechanisms of white matter fiber tracts.

Description

technical field [0001] The present invention relates to the technical field of digital image analysis and processing, in particular to a method for reconstructing white matter fiber bundles based on deep learning. Background technique [0002] In digital images, especially in microscopic images of biomedical brain white matter fibers, the brain white matter structure is composed of countless nerve fibers gathered inside the brain, which is lighter in color and is also called cerebral white matter. White matter fibers are made up of millions of "communicating tubes" that wrap around a long axon (also known as a process), which is coated with a white lipid called myelin, and these "white cables" The function of "is responsible for communicating the gray matter (neurons) in different brain regions and transmitting action potentials between neurons. The abnormal structure of the white matter of the brain is mainly manifested as the damage of the myelin sheath of the central ner...

AI Technical Summary

Problems solved by technology

The probabilistic fiber tract tracking algorithm improves the tracking accuracy. It can display smaller fiber tracts and calculate the bifurcation of fiber tracts. However, this algorithm is computationally intensive and time-consuming, and cannot meet the requirements of clinical applications.

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