Automatic classification correcting method based on shape characteristic

Abstract

The invention discloses an automatic sorting correction method based on shape characteristics for analytical processing of a flow cytometer. The method comprises the following steps: classifying and processing by an automatic sort algorithm to obtain a boundary curve of each cell according to scatter diagram data analyzed by flow cytometry; selecting a curve model equation to fit the boundary curves of each blood cell; computing standard curve parameters obtained by fitting; and comparing the standard parameters with pre-counted parameter empirical scope, judging whether the sort algorithm result is right and prompting. In the automatic sorting correction method based on the shape characteristics, a judgment mode of fitting curve parameters is adopted to compare with the pre-counted parameter empirical scope for correction, thus facilitating counting and graphic processing of cell detection data in the process of flow cell detection with more accurate processing result, and also facilitating the elimination and alarm processing of wrong detection results.

Description

technical field [0001] The invention relates to a method for cell detection, in particular to a method for automatically classifying, correcting and alarming when detecting various cells. Background technique [0002] In the prior art, in some blood cell classification devices based on flow cytometry, automatic classification algorithms are often used to classify blood cells. Usually, some classified categories have relatively fixed shape characteristics, and these shape characteristics are relatively fixed. . [0003] The blood cell analyzer based on flow cytometry uses flow technology to collect signals of different properties of the detected object (usually blood cells), such as the volume signal and complexity signal of white blood cells, and draws the collected signals into two dimensions or a three-dimensional scatterplot, such as figure 1 shown. [0004] Divide the scatter diagram into multiple areas. If the multiple parameter signals of the cells fall in the same ...

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Problems solved by technology

[0005] The traditional method is to classify using fixed boundaries on the scatter plot, but the disadvantage of fixed boundary classification is that it cannot be adjusted for different samples. If some samples appear to be significantly different from the classification characteristics expressed by the fixed boundaries, the tested The statistical characteristics of the sample will not reflect its real situation, leading to wrong conclusions based on the statistical results of the measurement

[0006] Due to the above shortcomings of the fixed boundary classification method, in some high-end blood cell analysis instruments based on flow cytometry, an adaptive classification algorithm is usually used to divide the blood cell categories to solve the defects of the fixed boundary classification algorithm; but in When the automatic classification algorithm is introduced in the classification of blood cells, another problem is also introduced: since the automatic classification algorithm may regard all kinds of classifications as meeting the requirements, the effectiveness of the automatic classification algorithm for each calculation result cannot be guaranteed

However, none of these patent documents mentions how to verify the validity of the classification results after the classification calculation is completed. Therefore, after the automatic classification algorithm recognizes an analysis result, it may detect the abnormal situation as a normal situation, resulting in detection mistakes

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