A cancer cell tracking method based on deep learning detection

Abstract

The invention provides a cancer cell tracking method based on deep learning detection. The method comprises the steps of: firstly, preprocessing data sets: using pascal_voc data sets to calibrate pictures and obtain picture tags with target classes and target positions; secondly, using a Faster R-CNN network: using the tags of the pictures obtained after data preprocessing and the data sets as input of the Faster R-CNN network, and obtaining a final detection model after network training; thirdly, tracking targets: after the obtained final cancer cell detection model has finished the detectionof cancer cells and is capable of locating the position of each target, judging the number of the targets accurately and realizing matching association of the cancer cells and target tracking by using a tracking algorithm. The method has the advantages of high accuracy and high multi-target tracking efficiency for cancer cells.

Description

technical field [0001] The invention belongs to the field of multi-target tracking, and designs a cancer cell tracking method based on deep learning detection. Background technique [0002] In the field of biology and medicine, observing cell morphology and observing the reaction of cells in a drug environment plays a very important role in studying the behavioral properties of cells to complete medical drug tests. It is also a frontier research direction in the field of image processing and pattern recognition. Traditional cell research is mainly to stain cells under a microscope, and then manually complete cell classification, counting, and tracking. These tasks are too complicated and require a lot of manpower and financial resources to obtain reliable results. In order to reduce the burden on staff and improve research efficiency, the researchers proposed to apply computer image processing technology to the field of target tracking, and use computers to track cells. [...

AI Technical Summary

Problems solved by technology

Although the TLD algorithm can effectively solve the problem of cell target loss, it still uses traditional features to describe the target in the detection and tracking of the target.

At the same time, traditional tracking methods are mostly used in single-target tracking. In multi-target tracking tasks, the search time efficiency increases exponentially with the increase in the number of targets, and cannot effectively solve the problems of new targets and old targets in multi-target tracking scenarios. Disappearance of targets and changes in the number of targets due to splitting or merging of targets

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