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Stem cell tracking method based on deep learning

A deep learning and stem cell technology, applied in interdisciplinary fields, can solve difficult problems such as stem cell tracking

Inactive Publication Date: 2018-07-06
SICHUAN UNIV
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0006] The above methods usually require a more complex image preprocessing process to achieve higher cell segmentation accuracy; better feature learning methods are needed to optimize the parameter model; complex manual parameter setting and adjustment are required to adapt to the diversity of cell movement , it is difficult to apply to the actual cell tracking scene to achieve real-time stem cell tracking

Method used

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  • Stem cell tracking method based on deep learning
  • Stem cell tracking method based on deep learning
  • Stem cell tracking method based on deep learning

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Embodiment 1

[0018] A stem cell tracking method and system based on deep learning technology, comprising the following steps:

[0019] Preparation Steps: Data preparation phase, which includes:

[0020] Microscopic image sequence input of stem cells to be tracked;

[0021] To calibrate the position of the stem cells to be tracked, use a rectangular calibration frame to calibrate the target stem cells so that they are located in the center of the calibration frame, and take appropriate values ​​for the width and height of the rectangular frame so that it can cover the target stem cells.

[0022] Step 1: cell image sampling and recognition model training, which includes:

[0023] Sampling the image of the target cell based on the position of the current target cell calibration frame, which is divided into two parts: positive sampling and negative sampling, such as image 3 , as shown in the schematic diagram of cell image sampling, where:

[0024] Positive sampling is centered on the cente...

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Abstract

The invention discloses a stem cell tracking method based on the deep learning technology and a stem cell tracking system based on the deep learning technology. The method / system comprises the steps that 1: a cell tracking algorithm based on the deep learning technology is put forward; 2: a cell division detection algorithm based on the deep learning technology is put forward; and 3: a set of stemcell real-time tracking and division detection system is established. The invention relates to the fields of deep learning, cell tracking, division detection and the neural network. According to themethod, automatic tracking of stem cell movement and division time detection of the movement process can be realized by using the image analysis technology based on deep learning by aiming at stem cell microscopic image input based on the stem cell to be observed calibrated by the first frame so as to provide the great analysis tool for the stem cell research work. Real-time automatic tracking method of stem cell movement can be realized so that the method and the system have great generalization and tracking accuracy and can be applied to the tracking task of other types of cells through proper adjustment.

Description

technical field [0001] The invention belongs to the interdisciplinary field of microscopic image processing, computer and deep learning fields. Background technique [0002] With the development of medical technology, it has become possible to use stem cells to repair damaged and diseased tissues, and scientific research on stem cells has become an important direction in the field of regenerative medicine research. Among them, the use of cell tracking technology to track life activities such as stem cell movement, transformation, and division in real time based on stem cell microscopic images can help researchers understand cell behavior, analyze cell morphological characteristics, and better understand various life phenomena. It is of great significance to the research and development of biomedicine and cell biology. [0003] Most of the existing stem cell tracking methods require complicated manual intervention processes, such as input data preprocessing, algorithm manual...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/69G06F18/214
Inventor 毛华章毅汪洋旭
Owner SICHUAN UNIV
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