Automatic focusing method for microscope cell glass slide scanning based on machine learning

An automatic focusing and machine learning technology, applied in microscopes, optics, instruments, etc., can solve the problems of slow calculation speed, slow focusing speed, and large amount of calculation.

Active Publication Date: 2016-06-08
深圳市东汇精密机电有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the existing problems of slow focusing speed, large amount of calculation, and slow calculation speed, and propose a machine learning-based automatic focusing method for scanning microscope cell slides

Method used

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  • Automatic focusing method for microscope cell glass slide scanning based on machine learning
  • Automatic focusing method for microscope cell glass slide scanning based on machine learning
  • Automatic focusing method for microscope cell glass slide scanning based on machine learning

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Experimental program
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specific Embodiment approach 1

[0026] Specific implementation mode one: combine Figure 5 To illustrate this embodiment, a machine learning-based automatic focusing method for scanning microscope cell slides in this embodiment is specifically prepared according to the following steps:

[0027] Step 1. Start;

[0028] Step 2. Select spiral scan, serpentine scan or random serpentine scan and the number of areas to be scanned according to the situation of the slide;

[0029] The scanning method is determined by the user according to the number of cells in the slide; if the number of cells in the slide is sparse, choose helical scanning; if the number of cells in the slide is large and the preparation is uniform, choose serpentine scanning; it needs to be completed within three minutes For scanning, choose random serpentine scanning;

[0030] Step 3. Before scanning the whole slide, by controlling the loading platform to move along the XY axis according to the scanning method selected in step 2, randomly sele...

specific Embodiment approach 2

[0038] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the specific process of random serpentine scanning in the step two is:

[0039] The random probability P in the random serpentine scan is determined by the number of areas scanned each time, and the formula is:

[0040] p = q A

[0041] Wherein, q is the number of areas to be scanned determined in step 2, and A is the number of scannable areas of the slide;

[0042]In the process of snake-like movement, a probability prediction is performed before each position scan. The probability prediction process is: generate a random number between 0 and 1. If it is less than P, it is selected for prediction, otherwise it is a prediction Not selected, if it is predicted to be selected, scan this area, otherwise skip scanning, and control the loading platform to move along the XY axis to the next image area to be collecte...

specific Embodiment approach 3

[0044] Specific embodiment 3: The difference between this embodiment and specific embodiment 1 or 2 is that in the step 3, before performing the full slide scanning of the glass slide, the scanning mode selected in step 2 is controlled to move along the XY axis by controlling the loading platform , randomly select 5 areas on the slide to sample, each area gets the clearest photo by focusing, and then calculate the threshold value of the low gray value statistical method (LGV) based on the 5 clearest photos, and focus with variable step climbing method The threshold in the strategy and the three step sizes that control the movement of the loading platform along the Z axis, the three step sizes are: the minimum step size s 1 , small step size s 2 , large step size s 3 ; The specific process is:

[0045] Step 31. By controlling the loading platform to move up and down along the Z axis to the vicinity of the effective image of the slide, set it as the starting position, search u...

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Abstract

The invention discloses an automatic focusing method for microscope cell glass slide scanning based on machine learning to solve the problems that the existing focusing speed is low, calculated amount is large and the calculation speed is low. The method specifically comprises the steps that firstly, the process begins; secondly, spiral scanning, S-shaped scanning or random S-shaped scanning and the number of areas to be scanned are selected according to the condition of a glass slide; thirdly, the threshold value, threshold and three step lengths of a low-gray value statistic law are worked out; fourthly, an object support is controlled to move to the next area with images to be collected along an XY axis according to the selected scanning mode; fifthly, the object support is controlled to arrive at the focus point, and a camera collects images of a current area; sixthly, whether the number of the collected images in the current area is equal to the number of the areas to be scanned in the second step or not is judged, if yes, a seventh step is executed, and if not, the fourth step is executed; seventhly, the process is brought to a termination. The method is applied to the field of microscope focusing.

Description

technical field [0001] The invention relates to an automatic focusing method for scanning microscope cell slides based on machine learning. Background technique [0002] Microscope plays an important role in pathological examination. Whether it is cytopathology or histopathology, doctors need to make corresponding specimens into glass slides, stain them, and then observe them under a microscope to draw conclusions. The traditional method of manual image reading has brought heavy labor to doctors, and it is easy to cause misdiagnosis. In recent years, with the development of instrument automation and intelligence, automatic film reading technology has begun to appear and develop rapidly. The technology uses a computer to control the continuous movement of the microscope and take clear images under the microscope, and then analyze and identify abnormal cells. Due to the introduction of automatic control and machine learning, this technology can assist doctors, effectively re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G02B21/24
CPCG02B21/244
Inventor 何勇军梁隆恺赵晶
Owner 深圳市东汇精密机电有限公司
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