Abstract: A method for determining a total number of fibers in a muscle tissue based on panoramic scanning is described. A muscle tissue slide is panoramically scanned to obtain a panoramic digital slide using a panoramic scanner, and the panoramic digital slide obtained is segmented, counted and summed to realize the statistical effect on the total number of fibers in the muscle tissue; compared with dependence on an optical microscope, 5-10 fields of view of an image are represented as histological characteristics of the muscle fiber of an biological individual, and further compared with a method using a product of average muscle fiber density and muscle slide area as an estimated value of the total number of fibers of the slide, the present disclosure can realize full-field statistics of the muscle tissue, with a smaller error and a more accurate result.

Abstract: The present disclosure discloses a brain atlas individualization method and system based on magnetic resonance and a twin graph neural network. Firstly, a feature is extracted from resting-state functional magnetic resonance imaging (rs-fMRI) by utilizing functional connectivity based on a region-of-interest, and at the same time, Fisher transformation and exponential transformation are performed on the feature; secondly, a corresponding adjacent matrix is extracted from T1-weighted magnetic resonance data in a data set; and then the twin graph neural network is designed for training and testing with the transformed feature and the adjacent matrix as inputs and a group atlas label and a sampling mask as outputs.

Abstract: Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW? matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination.

Abstract: Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.