Cell Tracking Method Based on Local Graph Matching and Convolutional Neural Networks

Abstract

The invention discloses a cell tracking method based on local graph matching and convolutional neural network. The method includes: S1. Segmenting cell images by using the watershed method; S2. Building and training the convolutional neural network, using the trained The convolutional neural network extracts the depth similarity of the cell pair to be matched; S3, extracts the similarity of the local triangle image of the cell pair to be matched in the cell segmentation image; S4, combines the depth similarity of the cell pair to be matched with the similarity of the local triangle image degree, establish a similarity matrix, and take the cell pair corresponding to the maximum value of the similarity matrix as the seed cell; S5, starting from the obtained seed cell, use the neighborhood cell diffusion method to match its adjacent cell pairs in turn until all cells The match is complete. The invention introduces the convolutional neural network to extract the depth similarity of the cell pair to be matched, and tracks the cells by combining the depth similarity and the local triangle similarity, which has the characteristics of wide application range and high tracking accuracy.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a cell tracking method based on local graph matching and convolutional neural network. Background technique [0002] In the research of biomedicine, the tracking of honeycomb closely arranged cells (such as plant meristematic cells, oral epithelial cells) plays a vital role. Currently, many cell tracking methods use a local graph matching model to detect seed cells, and then perform neighborhood cell diffusion growth from the seed cells. [0003] The steps of the existing cell tracking method to automatically track cells are as follows: [0004] a. Cell image segmentation: use the watershed method to segment cell boundaries; [0005] b. Local graph features: In the corresponding local graphs including central cells and adjacent cells, extract angle features, area features, and distance features to form local graph features; [0006] c. Multiple groups of seed cells: u...

AI Technical Summary

Problems solved by technology

First of all, the manual extraction of local image features by the above method is insufficient in discrimination, and is not suitable for image sequences with large time intervals; second, the accuracy of multiple sets of seed cells extracted by the above method is insufficient, which in turn affects the tracking accuracy.

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