Cell tracing method based on deep neural network

Abstract

The invention discloses a cell tracing method based on a deep neural network, relates to the technical field of image processing, and solves the problem of tracing a cell in a continuous microscope cell picture sequence. The method comprises the steps of collecting cell picture data, cutting out cell data, extracting characteristics of the cell by using a convolutional neural network, and acquiring an initialized multitask observation model; giving a first picture and a cell position of the microscope picture sequence, acquiring a candidate cell position close to the cell position in the first picture from the next frame of picture; sampling a positive sample and a negative sample on the first picture, training the initialized multitask observation model, and predicting the candidate cell position, thus obtaining a predicted similarity probability value of the cell; and predicting the candidate cell position with the largest similarity probability value to be the cell position of the next frame of picture, storing the largest predicted similarity probability value and the corresponding cell position in a model update strategy, and comparing with a threshold value. The cell tracing method provided by the invention is used for tracing the cell.

Description

technical field [0001] A cell tracking method based on a deep neural network, used for cell tracking, involves technical fields such as biomedicine, machine vision, image processing, and artificial intelligence. Background technique [0002] Visual tracking is an important research topic in the field of computer vision. The purpose of visual tracking is to track the trajectories of single or multiple objects, which has been widely used in many practical vision tasks, such as video surveillance, autonomous driving systems, and biological cell lineage analysis. As a typical visual tracking application, the goal of cell tracking is to track cells directly from microscopic image sequences. Through the results of cell tracking, we can study cell behavior, further construct cell lineage and analyze cell morphology. Therefore, cells with automated tracking methods are crucial. [0003] The challenges of cell tracking can be summarized into four categories. The first challenge i...

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Problems solved by technology

[0006] The present invention provides a cell tracking method based on a deep neural network to solve the problems of cell deformation, cell behavior, cell living environment and cell image resolution under the microscope in the prior art in the continuous microscope cell picture sequence. The problem of poor cell tracking effect due to low rate

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