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CN108918398A - A kind of circulating tumor cell detection method - Google Patents

A kind of circulating tumor cell detection method Download PDF

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CN108918398A
CN108918398A CN201810480756.9A CN201810480756A CN108918398A CN 108918398 A CN108918398 A CN 108918398A CN 201810480756 A CN201810480756 A CN 201810480756A CN 108918398 A CN108918398 A CN 108918398A
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毛磊
郑驰
萨尔瓦多·加西亚·博纳
张克奇
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YONGXIN OPTICS CO Ltd NINGBO
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Abstract

本发明涉及一种循环肿瘤细胞检测方法，其特征在于：包括如下步骤：(1)、通过荧光显微镜采集三个不同通道的荧光图像，三个不同通道对应的荧光图像分别为绿色荧光图像、红色荧光图像和蓝色荧光图像；(2)、对采集的绿色荧光图像、红色荧光图像和蓝色荧光图像分别进行初始化处理；步骤、得到三幅剔除了干扰细胞的绿色荧光图像、红色荧光图像和蓝色荧光图像；(4)、将(3)所得到的剔除了干扰细胞的绿色荧光图像、红色荧光图像和蓝色荧光图像进行合成。与现有技术相比，本发明对细胞层面分析处理，能够解决传统显微图像在像素层面处理产生的大量空洞和噪声问题，可以有效的去除由于噪生所带来的误检，从而提高检测的准确性。

The invention relates to a method for detecting circulating tumor cells, which is characterized in that it comprises the following steps: (1) collecting fluorescence images of three different channels through a fluorescence microscope, and the fluorescence images corresponding to the three different channels are green fluorescence images, red fluorescence images, and red fluorescence images respectively. Fluorescence image and blue fluorescence image; (2), the green fluorescence image of acquisition, red fluorescence image and blue fluorescence image are initialized respectively; Step, obtain three green fluorescence images, red fluorescence images and A blue fluorescent image; (4), combining the green fluorescent image, red fluorescent image and blue fluorescent image obtained in (3) without interfering cells. Compared with the prior art, the present invention analyzes and processes the cell level, which can solve the problem of a large number of voids and noise generated by traditional microscopic image processing at the pixel level, and can effectively remove false detections caused by noise, thereby improving detection accuracy.

Description

A kind of circulating tumor cell detection method
Technical field
The present invention relates to a kind of circulating tumor cell detection methods.
Background technique
Cancer is the second largest assailant of current human beings worldwide's death, is only second to traffic accident.Many experts and scholars are in the world In the molding reason and method of discrimination early period of research cancer.If cancer can find in time in early stage and take corresponding treatment Measure is that available effective control reduces dead probability.A kind of current novel cancer cell method of discrimination early period is It is widely used in --- circulating tumor cell detects (Circulating Tumer Cell, CTC).Usually differentiating process In, the blood of patient can be extracted and manufacture into sample, put and observe under the microscope.These blood samples are usually by three kinds of dyestuffs It is dyed, and is observed with corresponding three channels of fluorescence microscope.Electronic fluorescence microscope is commonly to acquire Tool can guarantee that the image of acquisition covers entire sample using image mosaic technology and Autofocus Technology.But due to lacking Few effective digital method of discrimination, staff usually will do it artificial detection.But the efficiency of artificial detection is extremely low, and quasi- True rate is not high.Thus leverage the accuracy and practicability of this kind of method detection.
Summary of the invention
It is a kind of accurately swollen with efficient circulation the technical problem to be solved by the present invention is to be provided for the above-mentioned prior art Oncocyte detection method.
The present invention solves technical solution used by above-mentioned technical problem：A kind of circulating tumor cell detection method, It is characterized in that：Include the following steps
Step (1), the fluorescent image that three different channels are acquired by fluorescence microscope, three different channels are corresponding glimmering Light image is respectively green fluorescence image, red fluorescence images and blue-fluorescence image；
Step (2), by step (1) acquisition green fluorescence image, red fluorescence images and blue-fluorescence image respectively into Row initialization process：
Step (2-1) obtains green fluorescence image and red fluorescence images and blue first with maximum variance between clusters Initialization area in fluorescent image where each cell；
Step (2-2), the initialization to all acquisitions in green fluorescence image, red fluorescence images and blue-fluorescence image Region carries out median filtering, and the convolution kernel size of filtering is N*N, and the value of N is 2~5, respectively obtains filtered green fluorescence Image and red fluorescence images and blue-fluorescence image；
Step (2-3), using watershed algorithm to filtered green fluorescence image, red fluorescence images and blue-fluorescence Image is split processing, then records the position of each cell split, area and average brightness, thus Respectively obtain the position that the cell come out is divided in green fluorescence image, red fluorescence images and blue-fluorescence image, area And average brightness；
Step (3) will be divided in the resulting green fluorescence image of step (2), red fluorescence images and blue-fluorescence image The average brightness for all cells for cutting out carries out then finding out position in preceding 10% cell respectively from bright to dark sequence respectively In the average brightness of the cell of lower critical value, as positive cell brightness discrimination threshold, find out in rear 10% cell positioned at upper The average brightness of critical value cell, as negative cells brightness discrimination threshold；The judgment criteria of positive cell is the flat of the cell Equal brightness is more than or equal to positive cell brightness discrimination threshold；The judgment criteria of negative cells be the cell average brightness be less than etc. In negative cells brightness discrimination threshold；
Step (4), according to the judgment principle of step (3), find the negative cells in green fluorescence image, record green is glimmering The cell position of all non-negative cells in light image, and rejected；Simultaneously according to institute in the green fluorescence image of record The cell position for the non-negative cells having rejects the cell of the same position in red fluorescence images and blue-fluorescence image, and Update the cell distribution in red fluorescence images and blue-fluorescence image；Then, it also according to the judgment principle of step (3), looks for To the positive cell in blue-fluorescence image, the cell position of all non-positive cells in blue-fluorescence image is recorded, and is given It rejects, while rejecting the cell of the same position in green fluorescence image and red fluorescence images, and update green fluorescence image With the cell distribution in red fluorescence images；Finally, being found in red fluorescence images also according to the judgment principle of step (3) Positive cell, record the position of all non-positive cells in red fluorescence images, and rejected, while rejecting glimmering in green The cell of same position in light image and blue-fluorescence image, and update the cell in green fluorescence image and blue-fluorescence image Distribution；Green fluorescence image, red fluorescence images and the blue-fluorescence image of interference cell are eliminated to obtain three width；
Step (5), by step (4) it is obtained eliminate the interference green fluorescence image of cell, red fluorescence images and Blue-fluorescence image is synthesized, and the standard of synthesis has：
A, the cell existed simultaneously in green fluorescence image, red fluorescence images and blue-fluorescence image is retained；
B, it is retained in the cell that the cell area in blue-fluorescence image is greater than the cell area in red fluorescence images；
The cell for meeting a and b condition simultaneously is retained, these cells are output to as the circulating tumor cell detected In final examining report.
Compared with the prior art, the advantages of the present invention are as follows：The present invention is recycled by the method for Digital Image Processing Tumour cell is detected, and to the processing of cell level analysis, is able to solve what traditional micro-image was generated in pixel layer face treatment A large amount of cavities and noise problem can be removed effectively due to raw brought erroneous detection of making an uproar, while respectively to the figure in three channels Judgment criteria is defined as progress different disposal can be convenient user, to improve the accuracy of detection, and then improves the effect of detection Rate.
Detailed description of the invention
Fig. 1 is circulating tumor cell detection method flow chart in the embodiment of the present invention.
Specific embodiment
The present invention will be described in further detail below with reference to the embodiments of the drawings.
The present invention provides a kind of circulating tumor cell detection methods comprising following steps
Step (1), the fluorescent image that three different channels are acquired by fluorescence microscope, three different channels are corresponding glimmering Light image is respectively green fluorescence image, red fluorescence images and blue-fluorescence image；In the present embodiment, three differences of acquisition The colour filter module for the fluorescence microscope that the fluorescent image in channel utilizes is different, and corresponding excitation wavelength is 475nm, 555nm and 385nm, and the whole resulting image resolution ratio of lid fragmentation is scanned up to 11500*36100 using 20x scanning objective；
Step (2), by step (1) acquisition green fluorescence image, red fluorescence images and blue-fluorescence image respectively into Row initialization process：
Step (2-1) obtains green fluorescence image and red fluorescence images and blue first with maximum variance between clusters Initialization area in fluorescent image where each cell, maximum variance between clusters are also referred to as big law, and this method is routine side Method；
Step (2-2), the initialization to all acquisitions in green fluorescence image, red fluorescence images and blue-fluorescence image Region carries out median filtering, and the convolution kernel size of filtering is N*N, and the value of N is 2~5, preferably 3, respectively obtains filtered green Color fluorescent image and red fluorescence images and blue-fluorescence image；
Step (2-3), using watershed algorithm to filtered green fluorescence image, red fluorescence images and blue-fluorescence Image is split processing, then records the position of each cell split, area and average brightness, thus Respectively obtain the position that the cell come out is divided in green fluorescence image, red fluorescence images and blue-fluorescence image, area And average brightness；
Step (3) will be divided in the resulting green fluorescence image of step (2), red fluorescence images and blue-fluorescence image The average brightness for all cells for cutting out carries out then finding out position in preceding 10% cell respectively from bright to dark sequence respectively In the average brightness of the cell of lower critical value, as positive cell brightness discrimination threshold, find out in rear 10% cell positioned at upper The average brightness of critical value cell, as negative cells brightness discrimination threshold；The judgment criteria of positive cell is the flat of the cell Equal brightness is more than or equal to positive cell brightness discrimination threshold；The judgment criteria of negative cells be the cell average brightness be less than etc. In negative cells brightness discrimination threshold；
Step (4), according to the judgment principle of step (3), find the negative cells in green fluorescence image, record green is glimmering The cell position of all non-negative cells in light image, and rejected；Simultaneously according to institute in the green fluorescence image of record The cell position for the non-negative cells having rejects the cell of the same position in red fluorescence images and blue-fluorescence image, and Update the cell distribution in red fluorescence images and blue-fluorescence image；Then, it also according to the judgment principle of step (3), looks for To the positive cell in blue-fluorescence image, the cell position of all non-positive cells in blue-fluorescence image is recorded, and is given It rejects, while rejecting the cell of the same position in green fluorescence image and red fluorescence images, and update green fluorescence image With the cell distribution in red fluorescence images；Finally, being found in red fluorescence images also according to the judgment principle of step (3) Positive cell, record the position of all non-positive cells in red fluorescence images, and rejected, while rejecting glimmering in green The cell of same position in light image and blue-fluorescence image, and update the cell in green fluorescence image and blue-fluorescence image Distribution；Green fluorescence image, red fluorescence images and the blue-fluorescence image of interference cell are eliminated to obtain three width；
Step (5), by step (4) it is obtained eliminate the interference green fluorescence image of cell, red fluorescence images and Blue-fluorescence image is synthesized, and the standard of synthesis has：
A, the cell existed simultaneously in green fluorescence image, red fluorescence images and blue-fluorescence image is retained；
B, it is retained in the cell that the cell area in blue-fluorescence image is greater than the cell area in red fluorescence images；
The cell for meeting a and b condition simultaneously is retained, these cells are output to as the circulating tumor cell detected In final examining report.
The present invention can be removed effectively by the detection of cell level due to raw brought erroneous detection of making an uproar, to cell level Analysis processing, is able to solve a large amount of empty and noise problems that traditional micro-image is generated in pixel layer face treatment, and cellular layer For surface treatment by each cell as individual, the cell for only meeting specified conditions can just enter number system；Simultaneously respectively User can be convenient to the picture progress different disposal in three channels and define judgment criteria, so that the present invention has more practicability and standard True property.

Claims (1)

1.一种循环肿瘤细胞检测方法，其特征在于：包括如下步骤1. A method for detecting circulating tumor cells, characterized in that: comprising the following steps 步骤(1)、通过荧光显微镜采集三个不同通道的荧光图像，三个不同通道对应的荧光图像分别为绿色荧光图像、红色荧光图像和蓝色荧光图像；Step (1), collect fluorescence images of three different channels through a fluorescence microscope, and the fluorescence images corresponding to the three different channels are green fluorescence images, red fluorescence images and blue fluorescence images respectively; 步骤(2)、将步骤(1)采集的绿色荧光图像、红色荧光图像和蓝色荧光图像分别进行初始化处理：Step (2), the green fluorescent image, red fluorescent image and blue fluorescent image collected in step (1) are initialized respectively: 步骤(2-1)、首先利用最大类间方差法获得绿色荧光图像和红色荧光图像和蓝色荧光图像中每一个细胞所在的初始化区域；Step (2-1), first using the maximum between-class variance method to obtain the initialization area where each cell is located in the green fluorescence image, the red fluorescence image and the blue fluorescence image; 步骤(2-2)、对绿色荧光图像、红色荧光图像和蓝色荧光图像中所有获得的初始化区域进行中值滤波，滤波的卷积核大小为N*N，N的取值为2～5，分别得到滤波后的绿色荧光图像和红色荧光图像和蓝色荧光图像；Step (2-2), performing median filtering on all the initialization regions obtained in the green fluorescence image, red fluorescence image and blue fluorescence image, the size of the filtered convolution kernel is N*N, and the value of N is 2 to 5 , to obtain the filtered green fluorescence image, red fluorescence image and blue fluorescence image respectively; 步骤(2-3)、利用分水岭算法对滤波后的绿色荧光图像、红色荧光图像和蓝色荧光图像进行分割处理，然后将每一个分割出来的细胞的位置、面积和平均亮度记录下来，从而分别得到绿色荧光图像、红色荧光图像和蓝色荧光图像中被分割出来的细胞的位置、面积和平均亮度；Step (2-3), using the watershed algorithm to segment the filtered green fluorescence image, red fluorescence image and blue fluorescence image, and then record the position, area and average brightness of each segmented cell, so that Obtain the position, area and average brightness of the segmented cells in the green fluorescence image, red fluorescence image and blue fluorescence image; 步骤(3)、将步骤(2)所得的绿色荧光图像、红色荧光图像和蓝色荧光图像中被分割出来的所有细胞的平均亮度分别进行自亮到暗的排序，然后分别找出前10％的细胞中位于下临界值的细胞的平均亮度，作为阳性细胞亮度判别阈值，找出后10％的细胞中位于上临界值细胞的平均亮度，作为阴性细胞亮度判别阈值；阳性细胞的判断标准为该细胞的平均亮度大于等于阳性细胞亮度判别阈值；阴性细胞的判断标准为该细胞的平均亮度小于等于阴性细胞亮度判别阈值；Step (3), sort the average brightness of all cells segmented from the green fluorescence image, red fluorescence image and blue fluorescence image obtained in step (2) from bright to dark, and then find out the top 10% respectively The average brightness of the cells at the lower critical value in the cells is used as the brightness discrimination threshold of positive cells, and the average brightness of the cells at the upper critical value in the last 10% of the cells is found as the brightness discrimination threshold of negative cells; the judgment standard of positive cells is The average brightness of the cell is greater than or equal to the brightness discrimination threshold of positive cells; the judgment standard of negative cells is that the average brightness of the cell is less than or equal to the brightness discrimination threshold of negative cells; 步骤(4)、根据步骤(3)的判断原则，找到绿色荧光图像中的阴性细胞，记录绿色荧光图像中所有的非阴性细胞的细胞位置，并予以剔除；同时根据记录的绿色荧光图像中所有的非阴性细胞的细胞位置，剔除在红色荧光图像和蓝色荧光图像中相同位置的细胞，并更新红色荧光图像和蓝色荧光图像中的细胞分布；然后，同样根据步骤(3)的判断原则，找到蓝色荧光图像中的阳性细胞，记录蓝色荧光图像中所有非阳性细胞的细胞位置，并予以剔除，同时剔除在绿色荧光图像和红色荧光图像中相同位置的细胞，并更新绿色荧光图像和红色荧光图像中的细胞分布；最后，同样根据步骤(3)的判断原则，找到红色荧光图像中的阳性细胞，记录红色荧光图像中所有非阳性细胞的位置，并予以剔除，同时剔除在绿色荧光图像和蓝色荧光图像中相同位置的细胞，并更新绿色荧光图像和蓝色荧光图像中的细胞分布；从而得到三幅剔除了干扰细胞的绿色荧光图像、红色荧光图像和蓝色荧光图像；Step (4), according to the judging principle of step (3), find the negative cells in the green fluorescence image, record the cell positions of all non-negative cells in the green fluorescence image, and remove them; The cell position of the non-negative cell, remove the cells in the same position in the red fluorescence image and the blue fluorescence image, and update the cell distribution in the red fluorescence image and the blue fluorescence image; then, also according to the judgment principle of step (3) , find the positive cells in the blue fluorescence image, record the cell positions of all non-positive cells in the blue fluorescence image, and remove them, and remove the cells at the same position in the green fluorescence image and the red fluorescence image, and update the green fluorescence image and the distribution of cells in the red fluorescence image; finally, according to the judgment principle of step (3), find the positive cells in the red fluorescence image, record the positions of all non-positive cells in the red fluorescence image, and remove them, and remove them in the green Cells at the same position in the fluorescence image and the blue fluorescence image, and update the cell distribution in the green fluorescence image and the blue fluorescence image; thereby obtaining three green fluorescence images, red fluorescence images and blue fluorescence images that eliminate interfering cells; 步骤(5)、将步骤(4)所得到的剔除了干扰细胞的绿色荧光图像、红色荧光图像和蓝色荧光图像进行合成，合成的标准有：Step (5), the green fluorescent image obtained in step (4), the red fluorescent image and the blue fluorescent image are synthesized, and the synthetic standards are: a、同时存在于绿色荧光图像、红色荧光图像和蓝色荧光图像中的细胞被保留；a. Cells present in green fluorescence image, red fluorescence image and blue fluorescence image are preserved; b、在蓝色荧光图像中的细胞面积大于红色荧光图像中的细胞面积的细胞被保留；b. Cells whose cell area in the blue fluorescence image is larger than that in the red fluorescence image are retained; 同时满足a和b条件的细胞被保留，这些细胞作为检测到的循环肿瘤细胞，输出到最终的检测报告中。Cells that meet the conditions of a and b are retained, and these cells are output as detected circulating tumor cells in the final detection report.
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CN111429440A (en) * 2020-03-31 2020-07-17 上海杏脉信息科技有限公司 Microscopic pathological image cell sufficiency detection method, system, equipment, device and medium
CN111429440B (en) * 2020-03-31 2023-04-28 上海杏脉信息科技有限公司 Method, system, equipment, device and medium for detecting sufficiency of microscopic pathology image cells

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Application publication date: 20181130