CN105181649B - A kind of Novel free marking mode identifies cell instrument method - Google Patents
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Abstract
本发明公开了一种新型免标记模式识别细胞仪方法,包括:配制待测免标记细胞溶液并导入微流通道中或制成细胞悬液芯片;传导激光激发待测细胞形成分布于三维空间的散射光,散射光经过光学成像系统或不经过任何光学成像系统,被二维CMOS探测器探测并获取待测细胞对应的二维光散射图样;获取的二维光散射图样传输至模式识别分类系统,系统自动学习属于不同种类的已知细胞的二维光散射图样,并对未知细胞实现免标记、自动化识别。识别结果触发相应装置,实现免标记、自动化的细胞计数或细胞分类功能。本发明无需对细胞进行复杂的荧光染色,可自动化、免标记实现对待测细胞的识别、计数、分类,操作简便快捷,显著降低分析成本,应用范围广泛。
The invention discloses a novel label-free pattern recognition cytometer method, comprising: preparing a label-free cell solution to be tested and introducing it into a microfluidic channel or making a cell suspension chip; conducting laser light to excite the cells to be tested to form a scattering pattern distributed in a three-dimensional space Light, scattered light passes through the optical imaging system or does not pass through any optical imaging system, is detected by the two-dimensional CMOS detector and obtains the two-dimensional light scattering pattern corresponding to the cell to be tested; the obtained two-dimensional light scattering pattern is transmitted to the pattern recognition classification system, The system automatically learns the two-dimensional light scattering patterns of known cells belonging to different types, and realizes label-free and automatic identification of unknown cells. The recognition result triggers the corresponding device to realize label-free, automated cell counting or cell classification functions. The present invention does not require complicated fluorescent staining of the cells, can automatically and label-free realize the identification, counting and classification of the cells to be tested, is simple and quick to operate, significantly reduces the analysis cost, and has a wide range of applications.
Description
技术领域technical field
本发明涉及细胞分类识别,特别是利用免标记细胞仪获取细胞的光散射图样信息,然后对光散射图样进行模式识别,实现免标记、自动化细胞计数或分类功能。The invention relates to cell classification and identification, in particular to using a label-free cytometer to obtain light scattering pattern information of cells, and then performing pattern recognition on the light scattering pattern to realize label-free, automatic cell counting or classification functions.
背景技术Background technique
传统流式细胞仪可用于细胞的分析和分选。一般说来,传统流式细胞仪需对细胞进行染色处理,而荧光染色或者其他生物标记可能会对细胞产生一定的影响,特别是在活体细胞功能研究方面。其次,荧光测量所需光路复杂,这直接增加了仪器成本,而且荧光测量需要对仪器进行校准,操作复杂,需要专业人员。最后,在后期信号处理方面,由于荧光发射光谱之间可能存在相互重叠,需要进行补色等操作,而且现有常规流式细胞仪在后期细胞分类识别方面,只能根据预定的物理、化学特征参量范围识别相应的细胞亚群,缺乏自动的机器学习并进行分类识别的功能,特别是在免标记、自动化细胞分类识别方面。Traditional flow cytometry can be used for cell analysis and sorting. Generally speaking, traditional flow cytometry requires staining of cells, and fluorescent staining or other biomarkers may have certain effects on cells, especially in the study of the function of living cells. Secondly, the optical path required for fluorescence measurement is complicated, which directly increases the cost of the instrument, and fluorescence measurement requires calibration of the instrument, which is complicated to operate and requires professionals. Finally, in terms of signal processing in the later stage, due to the possible overlap of fluorescence emission spectra, operations such as color complementation are required, and the existing conventional flow cytometers can only classify and identify cells based on predetermined physical and chemical characteristic parameters. The scope identifies the corresponding cell subpopulations, and lacks the function of automatic machine learning and classification recognition, especially in label-free and automated cell classification recognition.
临床上宫颈癌的筛查主要借助于宫颈细胞学检查以及HPV免疫检测。对于宫颈细胞学检查而言,首先从宫颈组织上采集脱落的宫颈细胞,将其染色、制片,然后由临床病理学医师在显微镜下进行人工阅片。有经验的医师可较为准确的判别宫颈正常与癌变细胞,有时可能需要进行HPV检测和阴道镜活检以便确诊。宫颈细胞学检查过程步骤复杂,耗时长,并且人工阅片需要医师具有丰富的临床经验,阅片结果具有较强的主观性。HPV检测和阴道镜活检准确率高但较难普及。Cervical cancer screening is mainly based on cervical cytology and HPV immunodetection. For cervical cytology examination, firstly, exfoliated cervical cells are collected from cervical tissue, stained and made into slices, and then manually read under a microscope by clinical pathologists. Experienced physicians can more accurately distinguish between normal and cancerous cells in the cervix, and sometimes HPV testing and colposcopy biopsy may be required to confirm the diagnosis. The process of cervical cytology examination is complicated and time-consuming, and manual film reading requires doctors with rich clinical experience, and the results of film reading are highly subjective. HPV detection and colposcopy biopsy are highly accurate but difficult to popularize.
发明内容Contents of the invention
本发明公开了一种新型免标记模式识别细胞仪方法,在获得免标记细胞或细胞聚集群的二维光散射图样的基础上,对散射图样进行模式识别,达到免标记、自动化细胞计数与分类识别的目的。该方法在样本处理方面,克服了传统流式细胞仪需要进行荧光染色的缺点,实现了免标记的样本处理;在光路系统上克服了传统流式细胞仪光路复杂、成本高的缺点;在信号处理方面,创新性的采用了模式识别来进行散射图样的自动分类识别。以上技术的有机集成实现了细胞分类方法上的创新。在使用效果方面,该创新方法可用于细胞聚集群数量的快速识别,也对正常宫颈细胞与癌变的宫颈HeLa细胞实现了免标记识别。The invention discloses a novel label-free pattern recognition cytometer method. On the basis of obtaining a two-dimensional light scattering pattern of label-free cells or cell aggregates, pattern recognition is performed on the scattering pattern to achieve label-free, automatic cell counting and classification purpose of identification. In terms of sample processing, this method overcomes the shortcomings of traditional flow cytometers that require fluorescent staining, and realizes label-free sample processing; it overcomes the shortcomings of traditional flow cytometers in terms of optical path complexity and high cost; In terms of processing, pattern recognition is innovatively used for automatic classification and recognition of scattering patterns. The organic integration of the above technologies has realized the innovation in the method of cell classification. In terms of application effect, this innovative method can be used for rapid identification of the number of cell aggregates, and also achieves label-free identification of normal cervical cells and cancerous cervical HeLa cells.
一种新型免标记模式识别细胞仪方法,具体方案包括以下步骤:A novel label-free pattern recognition cytometer method, the specific scheme includes the following steps:
配制出待测免标记细胞溶液并导入微流通道中或制成细胞悬液芯片;Prepare the label-free cell solution to be tested and introduce it into the microfluidic channel or make a cell suspension chip;
激光光源发出的光经过四倍物镜耦合进入光纤,光纤传导激光并激发微流通道或细胞悬液中的单细胞或多细胞聚集群,形成分布于三维空间的散射光;The light emitted by the laser light source is coupled into the optical fiber through the quadruple objective lens, and the optical fiber transmits the laser light and excites single cells or multi-cell aggregates in the microfluidic channel or cell suspension, forming scattered light distributed in three-dimensional space;
散射光经过光学成像系统,被二维CMOS探测器探测并获取待测细胞对应的二维光散射图样;The scattered light passes through the optical imaging system and is detected by the two-dimensional CMOS detector to obtain the two-dimensional light scattering pattern corresponding to the cells to be tested;
或者,散射光无需经过光学成像系统,被二维CMOS探测器探测并获取待测细胞对应的二维光散射图样;Alternatively, the scattered light is detected by a two-dimensional CMOS detector without passing through an optical imaging system, and a two-dimensional light scattering pattern corresponding to the cells to be tested is obtained;
获取的二维光散射图样传输至模式识别系统,该系统通过对已知不同细胞种类的二维光散射图样进行机器学习,实现对未知细胞的无标记、自动化识别;The obtained two-dimensional light scattering pattern is transmitted to the pattern recognition system, which realizes label-free and automatic identification of unknown cells by performing machine learning on two-dimensional light scattering patterns of known different cell types;
识别结果用于触发免标记模式识别细胞仪自动化细胞计数或细胞分类系统。The recognition results are used to trigger a label-free pattern recognition cytometer automated cell counting or cell sorting system.
进一步的,二维光散射图样获取对应的装置包括:光源系统,用于产生激光光源激发被测细胞的散射光;二维光散射图样探测记录系统,记录收集被测细胞的散射光;模式识别系统,通过数据处理及机器学习进行自动分类识别;细胞计数或细胞分类系统,包括数字计数器以及机械式分类装置。Further, the corresponding device for obtaining the two-dimensional light scattering pattern includes: a light source system for generating scattered light from a laser light source to excite the measured cells; a two-dimensional light scattering pattern detection and recording system for recording and collecting the scattered light of the measured cells; pattern recognition System for automatic classification and recognition through data processing and machine learning; cell counting or cell classification system, including digital counters and mechanical classification devices.
进一步的,散射光经过光学成像系统,获取待测细胞对应的二维光散射图样时,需要移动调整三维位移台寻找激光汇聚点,使激光光源经过四倍物镜后能以最佳耦合状态进入光纤中,光纤的另一端作为探针用于激发微流通道或细胞悬液中的单细胞或细胞聚集群。Further, when the scattered light passes through the optical imaging system to obtain the two-dimensional light scattering pattern corresponding to the cells to be measured, it is necessary to move and adjust the three-dimensional translation stage to find the laser convergence point, so that the laser light source can enter the optical fiber with the best coupling state after passing through the quadruple objective lens. In this method, the other end of the optical fiber is used as a probe to excite single cells or cell aggregates in microfluidic channels or cell suspensions.
进一步的,微流通道中待测溶液中的细胞被激光激发产生的散射光,经过光学成像系统或不经过光学成像系统;Further, the scattered light generated by the cells in the solution to be tested in the microfluidic channel is excited by the laser, passes through the optical imaging system or does not pass through the optical imaging system;
经过光学成像系统时,微流通道或细胞悬液中待测的细胞被激光激发产生散射光,该散射光通过十倍物镜观测,调整十倍物镜,观察到细胞的原图像,对该物镜去焦,在COMS二维探测器上获得散射光图样。When passing through the optical imaging system, the cells to be measured in the microfluidic channel or cell suspension are excited by the laser to generate scattered light. The scattered light is observed through the ten times objective lens, and the ten times objective lens is adjusted to observe the original image of the cells. focus, and obtain the scattered light pattern on the CMOS two-dimensional detector.
不经过光学成像系统,即不需要任何光学成像透镜,散射光经过一物理孔径,直接在CMOS平面上形成二维光散射图样。Without an optical imaging system, that is, without any optical imaging lens, the scattered light passes through a physical aperture to directly form a two-dimensional light scattering pattern on the CMOS plane.
进一步的,模式识别算法采用增强AdaBoost算法,对已检测的N个二维光散射图样,选取出N-1个已知分类的图样进行训练,获得一组模式识别的弱分类器,然后用此弱分类器组合对原先第N个图样进行测试,通过循环测试选出最强的弱分类器组合,最终得到对待测细胞分类识别的高准确率。Further, the pattern recognition algorithm adopts the enhanced AdaBoost algorithm to select N-1 patterns of known classification from the detected N two-dimensional light scattering patterns for training to obtain a set of weak classifiers for pattern recognition, and then use this The combination of weak classifiers tests the original Nth pattern, selects the strongest combination of weak classifiers through round-robin testing, and finally obtains a high accuracy rate for the classification and recognition of the cells to be tested.
进一步的,上述方法用于酵母细胞聚类群的分类。Furthermore, the above method is used for the classification of yeast cell clusters.
进一步的,上述方法用于正常宫颈细胞和癌变宫颈细胞的分类。Further, the above method is used for the classification of normal cervical cells and cancerous cervical cells.
本发明的有益效果:Beneficial effects of the present invention:
(1)本发明提出的新型免标记模式识别细胞仪装置简便,克服了传统流式细胞仪光路复杂、设备昂贵、操作繁琐等缺点,可简便快速获得二维光散射图样。(1) The new label-free pattern recognition cytometer proposed by the present invention has a simple device, overcomes the shortcomings of traditional flow cytometers such as complex optical path, expensive equipment, and cumbersome operation, and can easily and quickly obtain two-dimensional light scattering patterns.
(2)本发明采用的无标记技术,克服了传统流式细胞仪需要对细胞进行荧光染色,从而可能造成细胞样本损伤的问题,可避免荧光染色对细胞特别是活细胞功能造成的干扰。(2) The label-free technology adopted in the present invention overcomes the problem that traditional flow cytometers need to perform fluorescent staining on cells, which may cause damage to cell samples, and can avoid the interference of fluorescent staining on cells, especially the function of living cells.
(3)本发明提出的新型免标记模式识别细胞仪可实现对细胞或细胞群的免标记、自动化分类识别,即通过自动化的模式识别算法达到对待测细胞的分类识别。(3) The new label-free pattern recognition cytometer proposed by the present invention can realize label-free and automatic classification and recognition of cells or cell groups, that is, the classification and recognition of the cells to be tested can be achieved through an automated pattern recognition algorithm.
(4)本发明提出的新型免标记模式识别细胞仪泛化能力强,可广泛用于不同细胞的分类识别。(4) The new label-free pattern recognition cytometer proposed by the present invention has strong generalization ability and can be widely used in the classification and recognition of different cells.
(5)本发明分析过程可操作性强,可以选取恰当数目的弱分类器组成强分类器,尽可能的提高识别准确率。获得强分类后,操作人员只需输入检测图像就可以自动的获取分类识别结果。(5) The analysis process of the present invention is highly operable, and an appropriate number of weak classifiers can be selected to form a strong classifier to improve the recognition accuracy as much as possible. After the strong classification is obtained, the operator only needs to input the detection image to automatically obtain the classification recognition result.
(6)本发明所提出的免标记、自动化模式识别细胞术,可用于激发相应的物理计数器或者分选器,实现对细胞的计数、分选功能。(6) The label-free and automatic pattern recognition cytometry proposed by the present invention can be used to stimulate corresponding physical counters or sorters to realize the functions of counting and sorting cells.
(7)本发明提供了一种新型的进行细胞群数量区分的判断方法。(7) The present invention provides a novel method for judging the number of cell groups.
(8)本发明提供了一种新型的进行正常宫颈细胞和癌变的HeLa细胞分类的判断方法。(8) The present invention provides a novel judgment method for classifying normal cervical cells and cancerous HeLa cells.
附图说明Description of drawings
图1为本发明装置的结构及原理图,Fig. 1 is the structure and schematic diagram of device of the present invention,
图2(a)-图2(d)单个酵母细胞模拟图和实验散射图对比;Fig. 2(a)-Fig. 2(d) Comparison of the simulated diagram of a single yeast cell and the experimental scatter diagram;
图3(a)-图3(d)不同数量酵母细胞聚集群原图、散射图对比;Figure 3(a)-Figure 3(d) Comparison of the original and scatter diagrams of different numbers of yeast cell aggregates;
图4(a)-图4(d)正常宫颈细胞和HeLa细胞原图、散射图对比;Figure 4(a)-Figure 4(d) Comparison of the original and scatter diagrams of normal cervical cells and HeLa cells;
图1中:1、激光光源,2、四倍物镜,3、光纤耦合器,4、微流通道或细胞悬液芯片,5、十倍物镜或物理孔径,6、二维CMOS探测器,7、模式识别系统,8分类系统;In Figure 1: 1. Laser light source, 2. Quadruple objective lens, 3. Fiber coupler, 4. Microfluidic channel or cell suspension chip, 5. Ten-fold objective lens or physical aperture, 6. Two-dimensional CMOS detector, 7 , pattern recognition system, 8 classification system;
表1不同数量酵母细胞聚集群分类结果;Table 1 Classification results of different numbers of yeast cell aggregates;
表2正常宫颈细胞和HeLa细胞分类结果。Table 2 The classification results of normal cervical cells and HeLa cells.
具体实施方式:Detailed ways:
下面结合附图对本发明进行详细说明:The present invention is described in detail below in conjunction with accompanying drawing:
如图1所示,一种新型免标记模式识别细胞仪主要由光源系统、二维光散射图样探测记录系统、数据处理分类系统构成。其中光源系统包括激光光源1,四倍物镜2,光纤耦合器3,微流通道或细胞悬液芯片4;二维光散射图样探测记录系统包括十倍物镜或物理孔径5,二维CMOS探测器6;数据处理分类系统构成分析系统包括模式识别系统7,分类系统8。As shown in Figure 1, a new type of label-free pattern recognition cytometer is mainly composed of a light source system, a two-dimensional light scattering pattern detection and recording system, and a data processing and classification system. The light source system includes a laser light source 1, a quadruple objective lens 2, a fiber coupler 3, a microfluidic channel or a cell suspension chip 4; a two-dimensional light scattering pattern detection and recording system includes a tenfold objective lens or a physical aperture 5, and a two-dimensional CMOS detector 6. Data processing and classification system composition analysis system includes pattern recognition system 7 and classification system 8 .
本发明的二维光散射图样检测系统包括单细胞及细胞聚集群的光散射激发系统,微流控或细胞悬液芯片系统,以及二维光散射图样获取系统。本发明的数据处理分类系统采用模式识别算法来进行二维光散射图样的后期数据处理,实现了对单细胞以及细胞群的免标记、自动化分类识别。本发明以模式识别中的AdaBoost机器学习算法为例,但不局限于使用该特定的模式识别方法进行细胞光散射图样的识别。本发明不依赖于传统的荧光染色以及人工分类,通过对散射图样的模式识别,实现了对不同数量酵母细胞群以及宫颈细胞不同病理状态的免标记、自动化分类识别。本发明应用范围可推广到一般生物细胞的生理、病理分析。本发明所公开的新型免标记模式识别细胞仪方法,无需对细胞进行复杂的荧光染色,可免标记、自动化实现对待测细胞的分类、识别,以及后期的计数、分选等,操作简便快捷,结果准确可靠,显著降低分析成本,应用范围广泛。The two-dimensional light scattering pattern detection system of the present invention includes a light scattering excitation system for single cells and cell aggregates, a microfluidic or cell suspension chip system, and a two-dimensional light scattering pattern acquisition system. The data processing and classification system of the present invention uses a pattern recognition algorithm to perform post-data processing of two-dimensional light scattering patterns, and realizes label-free and automatic classification and recognition of single cells and cell groups. The present invention takes the AdaBoost machine learning algorithm in pattern recognition as an example, but is not limited to using this specific pattern recognition method to identify cell light scattering patterns. The invention does not rely on traditional fluorescent dyeing and manual classification, and realizes label-free and automatic classification and recognition of different numbers of yeast cell groups and different pathological states of cervical cells through pattern recognition of scattering patterns. The application range of the invention can be extended to the physiological and pathological analysis of general biological cells. The novel label-free pattern recognition cytometry method disclosed in the present invention does not require complex fluorescent staining of cells, and can automatically realize the classification and identification of the cells to be tested, as well as the counting and sorting of the cells to be tested without labeling, and the operation is simple and fast. The result is accurate and reliable, the cost of analysis is significantly reduced, and the application range is wide.
下面结合附图1对本发明进行具体操作步骤的详细说明:Below in conjunction with accompanying drawing 1 the present invention is carried out the detailed description of concrete operation step:
步骤一:配制待测细胞溶液,根据不同的待测细胞配制溶液的方法不尽相同。Step 1: prepare the cell solution to be tested, the method of preparing the solution is different according to different cells to be tested.
步骤二:将待测细胞溶液导入微流通道4或制成细胞悬液芯片4。Step 2: introducing the cell solution to be tested into the microfluidic channel 4 or making a cell suspension chip 4 .
步骤三:打开激光光源1,激光光源1采用532nm波长绿色激光二极管泵浦固体激光器(DPSS)。二极管泵浦固体激光器具有工作时间长、效率高、耗能低、热效应小、体积小等显著的优势。为了保证直径为1.0mm的激光光束能最大限度的耦合进入直径105μm、数值孔径(NA)为0.22的光纤中,本发明选择数值孔径为0.1的四倍物镜2以提高耦合效率。Step 3: Turn on the laser light source 1. The laser light source 1 uses a green laser diode-pumped solid-state laser (DPSS) with a wavelength of 532nm. Diode-pumped solid-state lasers have significant advantages such as long working time, high efficiency, low energy consumption, small thermal effect, and small size. In order to ensure that the laser beam with a diameter of 1.0mm can be coupled into an optical fiber with a diameter of 105 μm and a numerical aperture (NA) of 0.22 to the maximum extent, the present invention selects a quadruple objective lens 2 with a numerical aperture of 0.1 to improve the coupling efficiency.
步骤四:不断移动调整校准激光耦合器使激光光源经过四倍物镜2后,能以最佳耦合状态进入光纤耦合器3中光纤一端。光纤耦合器3的光纤另一端作为探针用于激发微流通道4或细胞悬液芯片4中的细胞或细胞群。Step 4: Constantly move and adjust the calibration of the laser coupler so that the laser light source can enter the fiber end of the fiber coupler 3 in the best coupling state after passing through the quadruple objective lens 2 . The other end of the optical fiber of the optical fiber coupler 3 is used as a probe to excite cells or cell groups in the microfluidic channel 4 or the cell suspension chip 4 .
步骤五:激光激发微流通道4或细胞悬液芯片4中待测细胞产生散射光。首先通过物镜观测,移动光纤去定位细胞,直到待测细胞处于激光束的中间并且能够完全激发侧向散射光。Step 5: The laser excites the cells to be tested in the microfluidic channel 4 or the cell suspension chip 4 to generate scattered light. First looking through the objective lens, move the fiber to position the cell until the cell to be measured is in the middle of the laser beam and fully excites the side scattered light.
步骤六:获取待测细胞的二维光散射图样。调整十倍物镜5,使得视野中的细胞图像最清晰,这时候得到的是细胞的原图像。本发明所要获取的图像不是细胞本身所成的像,而是其形成的二维散射光图样。在聚焦基础上,按照相同的方向和相同的距离调整物镜,即进行“去焦”,这时候在COMS二维探测器6平面上将形成二维光散射图样。本发明的COMS二维探测器尺寸为22.3×14.9mm,记录像素大约为1790万。采用COMS二维探测器有集成度高,功耗小,成本低,容易与其他芯片整合等优点。或者,细胞散射光不经过任何光学成像系统,即不经过任何透镜,亦可借助于物理孔径5,在CMOS探测器6平面上将形成二维光散射图样。Step 6: Obtain a two-dimensional light scattering pattern of the cells to be tested. Adjust the tenfold objective lens 5 so that the cell image in the field of view is the clearest, and what is obtained at this time is the original image of the cell. The image to be acquired by the present invention is not the image formed by the cells themselves, but the two-dimensional scattered light pattern formed by them. On the basis of focusing, adjust the objective lens in the same direction and the same distance, that is, "defocus", at this time, a two-dimensional light scattering pattern will be formed on the plane of the COMS two-dimensional detector 6. The size of the CMOS two-dimensional detector of the present invention is 22.3×14.9 mm, and the recording pixels are about 17.9 million. The use of CMOS two-dimensional detectors has the advantages of high integration, low power consumption, low cost, and easy integration with other chips. Alternatively, the light scattered by the cells does not pass through any optical imaging system, that is, does not pass through any lens, and can also use the physical aperture 5 to form a two-dimensional light scattering pattern on the plane of the CMOS detector 6 .
步骤七:将获得的COMS二维探测器6收集的二维光散射图样传输入模式识别系统7。Step 7: Transfer the obtained two-dimensional light scattering pattern collected by the COMS two-dimensional detector 6 to the pattern recognition system 7 .
步骤八:将获得的散射图样进行标准化处理,统一成220×220像素。标准化后的散射图样使用模式识别算法进行分类识别。Step 8: Standardize the obtained scattering patterns and unify them into 220×220 pixels. The normalized scatter patterns are classified and identified using a pattern recognition algorithm.
步骤九:模式识别系统7中,可以根据具体情况,使用相应的模式识别算来进行分类识别。本发明以AdaBoost算法为例,对已检测的N个散射图样,使用leave-one-out的方法,选取出其中的N-1个已知分类的图样进行训练,然后用留下的第N个图样对此弱分类器组合进行调试,从而获得一组弱分类器组合。接着,将原先N-1个训练样本其中的一个与原先留下的第N个图样更换,从而又训练得到一组弱分类器。按照这样的方法得到多层弱分类器,选取相应数量的多层弱分类器,通过将弱分类器的分类结果互补从而进行有效结合,构建一个强的分类器。该强分类器的所包含的多层弱分类器的数目,可以使得分类结果最优。当训练样本足够多,可以通过单次训练得到最终的分类器进行封装,从而满足分类识别要求。Step 9: In the pattern recognition system 7, the corresponding pattern recognition algorithm can be used to perform classification and recognition according to specific conditions. The present invention takes the AdaBoost algorithm as an example, uses the leave-one-out method for the detected N scattering patterns, selects N-1 known classification patterns for training, and then uses the remaining Nth The pattern debugs this combination of weak classifiers to obtain a set of weak classifier combinations. Next, replace one of the original N-1 training samples with the original Nth pattern, so as to train another group of weak classifiers. According to this method, multi-layer weak classifiers are obtained, a corresponding number of multi-layer weak classifiers is selected, and a strong classifier is constructed by effectively combining the classification results of the weak classifiers. The number of multi-layer weak classifiers contained in the strong classifier can make the classification result optimal. When there are enough training samples, the final classifier can be obtained through a single training for packaging, so as to meet the classification recognition requirements.
步骤十:通过模式识别系统自动学习待测细胞二维光散射图样,对未知细胞实现免标记、自动化识别。识别结果触发相应分类系统8装置,实现免标记、自动化的细胞计数或细胞分类功能。Step 10: Automatically learn the two-dimensional light scattering pattern of the cells to be tested through the pattern recognition system, and realize label-free and automatic identification of unknown cells. The recognition result triggers the corresponding sorting system 8 device to realize label-free, automatic cell counting or cell sorting functions.
实施例1Example 1
配置好浓度适宜的酵母溶液,使用本发明的装置得到酵母溶液的二维光散射图样,将实验结果和理论模拟结果进行对比验证。图2展示了单个酵母细胞形成的二维散射图样的理论模拟结果图2(a)和图2(b)与实验结果图2(c)和图2(d)的对比。酵母是单细胞微生物,细胞直径大约为3-6μm,实验的散射图呈现了两种不同的形态图2(c)和图2(d)。模拟中酵母细胞被假定为具有不同直径的球形颗粒,折射率为1.42,入射波长532nm,周围介质折射率为1.334,图2(a)中细胞直径选为3.8μm,(b)中直径为5.0μm。从图中可以观察到,实验结果与理论模拟结果无论是条纹数量还是条纹位置都互相吻合,可以验证本发明装置的准确性。Prepare a yeast solution with a suitable concentration, use the device of the present invention to obtain a two-dimensional light scattering pattern of the yeast solution, and compare and verify the experimental results and theoretical simulation results. Figure 2 shows the comparison of the theoretical simulation results of the two-dimensional scattering pattern formed by a single yeast cell in Figure 2(a) and Figure 2(b) with the experimental results in Figure 2(c) and Figure 2(d). Yeast is a single-celled microorganism with a cell diameter of about 3-6 μm. The scatter diagram of the experiment presents two different morphologies as shown in Figure 2(c) and Figure 2(d). In the simulation, yeast cells are assumed to be spherical particles with different diameters, the refractive index is 1.42, the incident wavelength is 532nm, and the refractive index of the surrounding medium is 1.334. The diameter of the cells in Figure 2 (a) is 3.8 μm, and the diameter in (b) is 5.0 μm. It can be observed from the figure that the experimental results and the theoretical simulation results are consistent with each other in terms of the number of fringes and the positions of the fringes, which can verify the accuracy of the device of the present invention.
实施例2Example 2
使用本发明的装置,从60组酵母细胞聚集群中得到60个相对应的聚集群二维光散射图样。其中,有30组是3个酵母细胞聚集的图样,另外30组是4个酵母细胞聚集的图样。每个散射图样的大小都是220×220像素,并且经过归一化处理,如图3(a)-图3(d)。Using the device of the present invention, 60 corresponding two-dimensional light scattering patterns of aggregates are obtained from 60 groups of yeast cell aggregates. Among them, 30 groups are patterns of 3 yeast cell aggregations, and the other 30 groups are patterns of 4 yeast cell aggregations. The size of each scattering pattern is 220×220 pixels, and has been normalized, as shown in Figure 3(a)-Figure 3(d).
本发明使用AdaBoost方法进行leave-one-out实验。具体实施步骤是:对59个已知分类(3个酵母细胞聚集或者4个酵母细胞聚集)的光散射图样进行训练,获得一组模式识别的弱分类器,然后用第60个图样对此弱分类器进行测试。记录测试数据的正确个数(CN),通过公式AR=CN/TN计算得到正确率(AR)。TN代表所有二维光散射图样的数量。如表1所示,研究发现当弱分类器的层数增加时,AR发生相应变化。本发明使用3层弱分类器,可以得到最大的正确率86.7%。其中对于3个酵母细胞聚集群AR为93.3%,4个酵母细胞聚集群AR为80%。The present invention uses the AdaBoost method to perform a leave-one-out experiment. The specific implementation steps are: train the light scattering patterns of 59 known classifications (3 yeast cell aggregations or 4 yeast cell aggregations), obtain a group of weak classifiers for pattern recognition, and then use the 60th pattern to weakly classify this The classifier is tested. Record the correct number (CN) of the test data, and calculate the correct rate (AR) through the formula AR=CN/TN. TN represents the number of all 2D light scattering patterns. As shown in Table 1, the study found that when the number of layers of the weak classifier increases, the AR changes accordingly. The present invention uses three layers of weak classifiers, and can obtain the maximum correct rate of 86.7%. Among them, the AR of 3 yeast cell aggregates was 93.3%, and the AR of 4 yeast cell aggregates was 80%.
表1 酵母细胞聚集群分类结果Table 1 Classification results of yeast cell aggregates
实施例3Example 3
本发明装置用于正常宫颈细胞和癌变宫颈细胞(HeLa细胞)的分类识别。对于宫颈细胞的分类识别,利用本发明的免标记模式识别细胞仪方法共获得了92个二维光散射图样,其中54个是正常宫颈细胞光散射图样,38个是HeLa细胞光散射图样,结果如图4(a)-图4(d)所示。在利用AdaBoost方法进行模式识别时,用91个散射图样来训练弱分类器,第92个用来测试。如表2所示,研究发现当弱分类器层数为7时分类的准确率AR达到最大值90.2%,其中正常宫颈细胞的正确率AR为90.7%,HeLa细胞的正确率AR为89.5%。正常宫颈细胞和癌变的宫颈细胞在400倍显微镜的观测下,它们外形相似,但是其内部结构已经发生了改变。二维光散射的图样包含细胞内部变化的信息,免标记模式识别细胞仪能够自动化的分类识别该两类细胞。本发明中90.2%的宫颈细胞分类识别正确率表明免标记模式识别细胞仪具有良好的临床应用前景。The device of the invention is used for classifying and identifying normal cervical cells and cancerous cervical cells (HeLa cells). For the classification and identification of cervical cells, a total of 92 two-dimensional light scattering patterns were obtained using the label-free pattern recognition cytometer method of the present invention, 54 of which were normal cervical cell light scattering patterns, and 38 were HeLa cell light scattering patterns. As shown in Figure 4(a)-Figure 4(d). When using the AdaBoost method for pattern recognition, 91 scattering patterns are used to train the weak classifier, and the 92nd one is used for testing. As shown in Table 2, the study found that when the number of weak classifier layers is 7, the classification accuracy AR reaches a maximum value of 90.2%, among which the correct rate AR of normal cervical cells is 90.7%, and the correct rate AR of HeLa cells is 89.5%. Normal cervical cells and cancerous cervical cells are observed under a 400-fold microscope. Their appearance is similar, but their internal structure has changed. The pattern of two-dimensional light scattering contains information about the changes inside the cell, and the label-free pattern recognition cytometer can automatically classify and identify the two types of cells. The 90.2% correct rate of cervical cell classification and recognition in the present invention indicates that the label-free pattern recognition cytometer has good clinical application prospects.
表2 宫颈细胞分类结果Table 2 Results of cervical cell classification
上述结合附图对本发明的具体实施方式进行的描述,并非对本发明保护范围的限制,在本发明的技术方案的基础上,本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围之内。The above description of the specific embodiments of the present invention in conjunction with the accompanying drawings is not a limitation of the protection scope of the present invention. On the basis of the technical solution of the present invention, various modifications or modifications can be made by those skilled in the art without creative labor. Deformation is still within the protection scope of the present invention.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1851729A (en) * | 2006-06-01 | 2006-10-25 | 北京中星微电子有限公司 | AdaBoost based characteristic extracting method for pattern recognition |
CN102016577A (en) * | 2008-05-09 | 2011-04-13 | 希森美康株式会社 | Blood analyzer, blood analysis method and hemolytic agent |
CN102270307A (en) * | 2011-06-24 | 2011-12-07 | 福州大学 | Automatic detection method of tumor cancerization cell fluorescence in situ hybridization (FISH) gene state |
WO2012000102A1 (en) * | 2010-06-30 | 2012-01-05 | The Governors Of The University Of Alberta | Apparatus and method for microscope-based label-free microflutdic cytometry |
CN103616356A (en) * | 2013-11-25 | 2014-03-05 | 大连海事大学 | Device and method for classifying microalgae in ship ballast water |
CN104568857A (en) * | 2015-01-29 | 2015-04-29 | 山东大学 | Method and device for novel two-dimensional light scattering static cytometer |
-
2015
- 2015-10-09 CN CN201510649334.6A patent/CN105181649B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1851729A (en) * | 2006-06-01 | 2006-10-25 | 北京中星微电子有限公司 | AdaBoost based characteristic extracting method for pattern recognition |
CN102016577A (en) * | 2008-05-09 | 2011-04-13 | 希森美康株式会社 | Blood analyzer, blood analysis method and hemolytic agent |
WO2012000102A1 (en) * | 2010-06-30 | 2012-01-05 | The Governors Of The University Of Alberta | Apparatus and method for microscope-based label-free microflutdic cytometry |
CN102270307A (en) * | 2011-06-24 | 2011-12-07 | 福州大学 | Automatic detection method of tumor cancerization cell fluorescence in situ hybridization (FISH) gene state |
CN103616356A (en) * | 2013-11-25 | 2014-03-05 | 大连海事大学 | Device and method for classifying microalgae in ship ballast water |
CN104568857A (en) * | 2015-01-29 | 2015-04-29 | 山东大学 | Method and device for novel two-dimensional light scattering static cytometer |
Non-Patent Citations (2)
Title |
---|
Development of a new method for cervical cancer cells determination using light scattering spectrum;Yan Yang etc.;《Proc. of SPIE》;20121231;第8412卷;841205-1-8 * |
Label-free analysis of single and multiple cells with a 2D light scattering static cytometer;Shanshan Liu etc.;《Proc. of SPIE》;20150430;第9328卷;图4 * |
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