CN107169556A - stem cell automatic counting method based on deep learning - Google Patents

stem cell automatic counting method based on deep learning Download PDF

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CN107169556A
CN107169556A CN201710339326.0A CN201710339326A CN107169556A CN 107169556 A CN107169556 A CN 107169556A CN 201710339326 A CN201710339326 A CN 201710339326A CN 107169556 A CN107169556 A CN 107169556A
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蒲晓蓉
王之骢
庞晋雁
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University of Electronic Science and Technology of China
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Abstract

一种基于深度学习的干细胞自动计数方法,细胞计数技术领域,包括如下步骤:S11:采用相差显微镜拍摄需要计数的细胞,生成干细胞图像。S12:图像预处理;对细胞图像进行降噪处理和光照均衡化处理得到光照均匀的干细胞图像。S13:去除相差显微镜拍摄过程中产生的细胞伪影。S14:对去除伪影后的干细胞图像进行分割,获取多个候选干细胞图像。S21:对分割出的多个候选细胞图像进行手工标记,建立训练集。S22:将训练集输入CNN进行训练。S23:统计干细胞计数结果。本发明解决了传统人工细胞计数消耗大量人力的缺点,也克服了流式计数法会破坏细胞生长环境的缺陷,通过拍摄的细胞图像进行计数,具有稳定、高效、自动、无损等特点。

An automatic stem cell counting method based on deep learning, in the field of cell counting technology, comprising the following steps: S11: using a phase-contrast microscope to photograph cells to be counted to generate a stem cell image. S12: image preprocessing; performing noise reduction processing and illumination equalization processing on the cell image to obtain a stem cell image with uniform illumination. S13: Remove cell artifacts generated during phase contrast microscopy. S14: Segment the stem cell image after artifact removal, and acquire multiple candidate stem cell images. S21: Manually mark the plurality of segmented candidate cell images to establish a training set. S22: Input the training set into CNN for training. S23: Counting the stem cell counting results. The invention solves the shortcomings of traditional manual cell counting that consumes a lot of manpower, and also overcomes the defect that the flow counting method will destroy the cell growth environment, and counts through the captured cell images, which has the characteristics of stability, high efficiency, automaticity, and non-destructiveness.

Description

基于深度学习的干细胞自动计数方法Automatic counting method of stem cells based on deep learning

技术领域technical field

本发明涉及细胞计数技术领域,尤其涉及基于深度学习的干细胞自动技术方法。The invention relates to the technical field of cell counting, in particular to an automatic stem cell technology method based on deep learning.

背景技术Background technique

随着人类科学的进步,越来越多的科研人员致力于揭示生命的奥秘。干细胞是可以自我复制增殖的一类细胞,在特定的情况下,可以分化成其他类型的细胞。因此针对干细胞的生长、增值、分化研究是细胞生物学中的一个重要研究方向。With the advancement of human science, more and more researchers are devoted to revealing the mysteries of life. Stem cells are a type of cell that can replicate and proliferate by itself, and under certain circumstances, can differentiate into other types of cells. Therefore, research on the growth, proliferation and differentiation of stem cells is an important research direction in cell biology.

准确的进行细胞计数对细胞的生长和分裂的研究工作有着重要的意义,现有的传统细胞研究方式,主要是由研究人员通过显微镜对细胞样本进行直接观察,不仅消耗大量时间,程序繁复,还存在严重的主观性。除此之外,为了研究干细胞的分裂、增殖以及分化过程,往往会对细胞进行染色以及添加荧光技术,会对细胞活性等造成一定影响。因此,如何保持多能干细胞在人工培育环境下的自然活性状态,使其不经干预的分裂成为了重点研究问题。利用显微观察技术,借助计算机视觉处理技术,使对干细胞图像的定量分析成为可能。Accurate cell counting is of great significance to the research work of cell growth and division. The existing traditional cell research method mainly uses researchers to directly observe cell samples through a microscope, which not only consumes a lot of time, but also has complicated procedures. There is serious subjectivity. In addition, in order to study the division, proliferation and differentiation process of stem cells, the cells are often stained and fluorescent technology is added, which will have a certain impact on cell activity. Therefore, how to maintain the natural active state of pluripotent stem cells in an artificial culture environment and make them divide without intervention has become a key research issue. Using microscopic observation technology and computer vision processing technology, it is possible to quantitatively analyze stem cell images.

因此,总体说来,现有技术中对细胞计数的方法有三种,下面将对这三类细胞计数法进行缺点说明。Therefore, generally speaking, there are three methods for cell counting in the prior art, and the disadvantages of these three types of cell counting methods will be described below.

第一种:库尔特计数法。库尔特计数法是在测定管中装入电解质溶液,将粒子群混悬在电解质溶液中,测定管壁上有一细孔,孔电极间有一定电压,悬浮在电解液中的颗粒随电解液通过小孔管时,取代相同体积的电解液,由于电阻发生改变使电流变化并记录在记录器上,最后可将电信号换算成粒径。可用该方法求得粒度分布。库尔特计数法可以将细胞视为粒子,因此被广泛应用在自动化的血细胞计数仪上,在医学上有着重大的意义。但是,此方法对于干细胞研究来说却并不适用。第一,无法使培养基上的干细胞通过计数仪,否则会破环干细胞生长的稳定环境,影响研究的结果。第二,该方法是通过统计手段获得溶液中的细胞总数,而对于干细胞研究来说细胞计数需要精确到个位,因此该方法并不适用。The first: Coulter counting method. The Coulter counting method is to put the electrolyte solution into the measurement tube, suspend the particle group in the electrolyte solution, there is a fine hole on the wall of the measurement tube, and there is a certain voltage between the holes and electrodes, and the particles suspended in the electrolyte will move with the electrolyte solution. When passing through the small hole tube, the same volume of electrolyte is replaced, and the current changes due to the change of resistance and is recorded on the recorder. Finally, the electrical signal can be converted into particle size. This method can be used to obtain the particle size distribution. The Coulter counting method can treat cells as particles, so it is widely used in automated blood cell counters and has great significance in medicine. However, this method is not suitable for stem cell research. First, the stem cells on the culture medium cannot pass through the counter, otherwise the stable environment for stem cell growth will be destroyed and the results of the research will be affected. Second, this method is to obtain the total number of cells in the solution by statistical means, but for stem cell research, the cell count needs to be accurate to the single digit, so this method is not applicable.

第二种:人工统计法;通过人工对显微镜下的细胞图像进行计数,有较高的计数准确性。该方法的工作量巨大,消耗大量宝贵的人力资源,数据量较大时该方法几乎没有实现的可能;此外由于人工的主观性,尽管计数相对精准,由于观察者专业水平的不一致,有可能对细胞的状态发生误判。The second method: artificial statistical method; by manually counting the cell images under the microscope, the counting accuracy is relatively high. The workload of this method is huge and consumes a lot of valuable human resources. When the amount of data is large, this method is almost impossible to realize. In addition, due to the subjectivity of manual work, although the counting is relatively accurate, due to the inconsistency of the professional level of the observers, it is possible to The state of the cell was misjudged.

第三种:通过流式细胞计数仪度细胞进行计数。流式细胞仪是比较早的用于细胞数量定量化分析的仪器。流式细胞仪是在细胞流动的状态下进行计数,首先使这些流动的细胞经过流式细胞仪的检测区域,在细胞经过这个区域的同时,用激光激发有特异性荧光标记的抗体,此时被激发的荧光抗体发射出一定波长的荧光,细胞仪探测到这些激发光并且将这些光信号转化为电信号,同时也测量细胞的一些物理和生化特征参数,根据激发光特征和这些物理和化学特征参数,将不同种类的细胞进行区分,并且分别对不同种类的细胞数量进行统计。但是,进入流式细胞仪的样本的实际空间位置已经被破坏,所以细胞仪的测量缺少空间位置信息,而样本的实际空间位置信息对于细胞的研究具有很重要的意义,这是因为在组织的微环境中,一类细胞从微环境中的一个位置移动到另一个位置很可能会严重的影响到组织或器官的状态。同时在进入细胞仪前的样本准备需要很大的人工参与量。因此,流失细胞计数仪也并不适用于干细胞的计数。The third method: counting cells by flow cytometer. Flow cytometry is an earlier instrument used for quantitative analysis of cell numbers. Flow cytometry counts in the state of cell flow. First, these flowing cells pass through the detection area of the flow cytometer. The excited fluorescent antibody emits fluorescence of a certain wavelength, and the cytometer detects the excitation light and converts the light signal into an electrical signal, and also measures some physical and biochemical characteristic parameters of the cell. The characteristic parameter distinguishes different types of cells, and counts the number of different types of cells. However, the actual spatial position of the sample entering the flow cytometer has been destroyed, so the measurement of the cytometer lacks spatial position information, and the actual spatial position information of the sample is very important for the study of cells, because in the tissue In the microenvironment, the movement of a class of cells from one location in the microenvironment to another is likely to seriously affect the state of the tissue or organ. At the same time, the sample preparation before entering the cytometer requires a lot of manual participation. Therefore, the loss cell counter is not suitable for counting stem cells.

发明内容Contents of the invention

本发明的目的在于:根据背景技术中可知,现有技术中存在如下技术问题:(1)尔特计数法会破环培养基上的干细胞生长的稳定环境,影响研究的结果;(2)人工统计法工作量巨大,消耗大量宝贵的人力资源,数据量较大时该方法几乎没有实现的可能,并且人工计数法具有一定的主观性,尽管计数相对精准,由于观察者专业水平的不一致,有可能对细胞的状态发生误判;(3)采用流式细胞计数仪计数会破坏样本的实际空间位置,且在进入细胞仪前的样本准备需要很大的人工参与量。为解决这三个问题,本发明提供一种针对干细胞数字图像的基于深度学习的干细胞计数方法。The purpose of the present invention is: according to the background technology, there are following technical problems in the prior art: (1) the Walter counting method can destroy the stable environment of stem cell growth on the culture medium, and affect the results of the research; (2) artificial The statistical method has a huge workload and consumes a lot of valuable human resources. When the amount of data is large, this method is almost impossible to realize, and the manual counting method has a certain degree of subjectivity. Although the counting is relatively accurate, due to the inconsistent professional level of the observers, there are Misjudgment of the state of cells may occur; (3) counting by flow cytometer will destroy the actual spatial position of the sample, and the sample preparation before entering the cytometer requires a lot of manual participation. In order to solve these three problems, the present invention provides a method for counting stem cells based on deep learning for stem cell digital images.

本发明的技术方案如下:Technical scheme of the present invention is as follows:

一种基于深度学习的干细胞自动计数方法,包括如下步骤:A method for automatically counting stem cells based on deep learning, comprising the steps of:

S1:采用相差显微镜拍摄需要计数的细胞,生成干细胞图像;S1: Use a phase-contrast microscope to photograph the cells to be counted to generate stem cell images;

S2:通过细胞分割技术对干细胞图像进行分割,得到潜在的多个候选干细胞图像;具体地,S2包括如下步骤:S2: Segment the stem cell image by cell segmentation technology to obtain multiple potential candidate stem cell images; specifically, S2 includes the following steps:

S21:图像预处理;对干细胞图像进行降噪处理和光照均衡化处理得到光照均匀的干细胞图像;S21: Image preprocessing; performing noise reduction processing and illumination equalization processing on the stem cell image to obtain a stem cell image with uniform illumination;

S22:去除相差显微镜拍摄过程中产生的细胞伪影;S22: removing cell artifacts generated during phase contrast microscopy;

S23:对去除伪影后的干细胞图像进行分割,获取多个候选干细胞图像;S23: Segment the stem cell image after artifact removal, and acquire multiple candidate stem cell images;

S3:细胞识别;具体地,S3包括如下步骤:S3: cell identification; specifically, S3 includes the following steps:

S31:对分割出的多个候选干细胞图像进行手工标记,建立训练集;S31: Manually mark the plurality of segmented candidate stem cell images to establish a training set;

S32:将训练集输入CNN进行训练,对每一张候选干细胞图像,CNN都会输出一个结果表示其是否为细胞;S32: Input the training set into CNN for training, and for each candidate stem cell image, CNN will output a result indicating whether it is a cell;

S32包括如下步骤:S32 comprises the following steps:

S33:统计所有的多个候选干细胞图像中干细胞数量,得出干细胞计数结果。S33: Counting the number of stem cells in all the plurality of candidate stem cell images to obtain a stem cell counting result.

优选地,对干细胞图像的降噪处理采用高斯滤波器,光照均衡化处理采用背景减除法。Preferably, a Gaussian filter is used for the denoising processing of the stem cell image, and a background subtraction method is used for the illumination equalization processing.

具体地,S22的具体过程为:Specifically, the specific process of S22 is:

设g为成像图像,f为原始图像,H为成像矩阵,C为背景模型,根据相差显微镜成像原理,将成像过程定义为一个线性模型:Suppose g is the imaging image, f is the original image, H is the imaging matrix, and C is the background model. According to the imaging principle of the phase contrast microscope, the imaging process is defined as a linear model:

g≈Hf+Cg≈Hf+C

若背景已去除,则模型可以简化为:If the background has been removed, the model can be simplified as:

g≈Hfg≈Hf

去伪影的过程就是从g恢复f的过程,使用一个约束二次函数来对f进行恢复:The process of removing artifacts is the process of restoring f from g, using a constrained quadratic function to restore f:

其中,L是定义空间像素邻居相似度的拉普拉斯矩阵,Λ是一个正对角矩阵,ws、wr是通过网格搜索学习得到权重不同的正则化项;Among them, L is the Laplacian matrix that defines the similarity of neighbors of spatial pixels, Λ is a positive diagonal matrix, w s and w r are regularization terms with different weights obtained through grid search learning;

上述公式没有封闭解,只能数值逼近,因此约束恢复函数f有非负解,将公式转化为求优化问题:The above formula has no closed solution and can only be approximated numerically. Therefore, the constraint restoration function f has a non-negative solution, and the formula can be transformed into an optimization problem:

O(f)=fTQf+2bTf+c s.t.f≥0O(f)=f T Qf+2b T f+c stf≥0

Q=HTH+wsL+wtQ=H T H+w s L+w t

b=-HTg-wtTf(t)+wrdiag(Λ)/2b=-H T gw tT f (t) +w r diag(Λ)/2

其中,c为常数项wt为时间一致性正则化的加权因子,f(t)为t时刻对应的f,s、r、Q、b为约束二次规划问题中对权重因子的习惯性表述;使用非负乘法更新算法得到f,得到去除伪影后的干细胞图像:Among them, c is a constant term w t is the weighting factor of time consistency regularization, f (t) is f corresponding to time t, s, r, Q, b are the customary expressions of weighting factors in constrained quadratic programming problems ; Use the non-negative multiplication update algorithm to get f, and get the stem cell image after artifact removal:

具体地,S23的具体过程为:Specifically, the specific process of S23 is:

S231:对去除伪影后的干细胞图像进行二值化处理,得到边缘清晰的干细胞图像;S231: Perform binarization processing on the stem cell image after artifact removal to obtain a stem cell image with clear edges;

S232:再计算干细胞图像中每个连通域的面积;S232: Recalculate the area of each connected domain in the stem cell image;

S233;舍弃面积低于阈值的部分,剩下的作为候选细胞区域,阈值根据图细胞图像分辨率来设定;S233: Discard the part whose area is lower than the threshold, and use the rest as the candidate cell area, and the threshold is set according to the resolution of the image cell image;

S234:对候选细胞区域进行切割,得到候选干细胞图像。S234: Cutting candidate cell regions to obtain candidate stem cell images.

具体地,S31的具体步骤为:Specifically, the specific steps of S31 are:

S311:将一部分分割出的干细胞图像设置分类标签,分为细胞和非细胞两类;S311: Set classification labels on part of the segmented stem cell images, and divide them into two types: cells and non-cells;

S312:以分类为细胞的样本作为正样本,以分类为非细胞的样本作为负样本,对正样本进行旋转扩充,对每张图片进行90°、180°、270°旋转;S312: Taking samples classified as cells as positive samples and samples classified as non-cells as negative samples, rotate and expand the positive samples, and rotate each picture by 90°, 180°, and 270°;

S313:对正样本中每一个原图像和旋转后的图像进行镜面转换,将一张正样本图像扩充为8张,将正样本及其扩充图像以及负样本共同组成训练集。S313: Perform mirror transformation on each original image and rotated image in the positive sample, expand one positive sample image into 8 images, and form the training set together with the positive sample, its expanded image and the negative sample.

具体地,S32的具体步骤为:Specifically, the specific steps of S32 are:

S321:输入数据为24×24像素点构成的矩阵,第一个特征图层采用5×5的窗口对输入图像进行卷积,得到每个特征图的尺寸为20×20;S321: The input data is a matrix composed of 24×24 pixels, the first feature layer uses a 5×5 window to convolve the input image, and the size of each feature map is obtained as 20×20;

S322:在第一个下采样层对卷积得到的特征图进行下采样操作,得到同样数量的特征图,尺寸减小为10×10;S322: Perform a downsampling operation on the feature map obtained by convolution in the first downsampling layer to obtain the same number of feature maps with a size reduced to 10×10;

S323:再进行一次卷积操作和下采样操作,最终得到一个1×2的输出结果,即候选图像是细胞或者非细胞;S323: Carry out another convolution operation and downsampling operation, and finally obtain a 1×2 output result, that is, the candidate image is a cell or a non-cell;

S324:将输出结果与训练集中对应的标签进行对比,将对比结果反馈回CNN,使用反向传播算法对CNN中神经元的权值进行更新,重复这个过程,识别的正确率会逐渐升高直到相对稳定,最后得到一个可用于候选图像分类的CNN。S324: Compare the output result with the corresponding label in the training set, feed back the comparison result to CNN, use the backpropagation algorithm to update the weights of neurons in CNN, repeat this process, and the recognition accuracy will gradually increase until Relatively stable, and finally a CNN that can be used for candidate image classification.

具体地,S33的具体过程为:将一张显微图像的所有候选细胞图像输入CNN进行分类,对每一张候选图像,CNN都会输出一个结果表示其是否是干细胞,然后统计所有被CNN判定为细胞图像的候选图像数量,得到输入显微图像的干细胞总数。Specifically, the specific process of S33 is: input all candidate cell images of a microscopic image into CNN for classification, and for each candidate image, CNN will output a result indicating whether it is a stem cell, and then count all cell images judged by CNN The number of candidate images of , to get the total number of stem cells in the input microscopic image.

采用上述方案后,本发明的有益效果如下:本发明主要利用了数字图象处理技术,对相差显微镜拍摄的干细胞图像进行细胞自动计数,彻底解决了传统人工细胞计数消耗大量人力的缺点,也克服了流式计数法会破坏细胞生长环境的缺陷,通过拍摄的细胞图像进行计数,具有稳定、高效、自动、无损等特点。After adopting the above scheme, the beneficial effects of the present invention are as follows: the present invention mainly utilizes the digital image processing technology to automatically count the stem cells taken by the phase contrast microscope, which completely solves the shortcomings of traditional manual cell counting that consumes a lot of manpower, and also overcomes the Overcoming the defect that flow cytometry will destroy the cell growth environment, counting through the captured cell images has the characteristics of stability, high efficiency, automatic, and non-destructive.

附图说明Description of drawings

图1为本发明中的经过降噪和去背景后,去除伪影之前的图像;Fig. 1 is the image before removing artifacts after noise reduction and background removal in the present invention;

图2为本发明中去除伪影后的图像;Fig. 2 is the image after artifact removal among the present invention;

图3为卷积神经网络模型结构示意图;Fig. 3 is a schematic diagram of the structure of the convolutional neural network model;

图4为具体实施例中局部感知原理的全连接和局部连接示意图;Fig. 4 is a schematic diagram of full connection and partial connection of the local perception principle in a specific embodiment;

图5为具体实施例中的权值共享示意图;Fig. 5 is a schematic diagram of weight sharing in a specific embodiment;

图6为卷积神经网络结构图。Figure 6 is a structural diagram of a convolutional neural network.

具体实施方式detailed description

下面结合具体实施例对本发明的方案进行进一步地详细说明。The solutions of the present invention will be further described in detail below in conjunction with specific embodiments.

基于深度学习的干细胞自动计数方法,包括如下步骤:The method for automatically counting stem cells based on deep learning comprises the following steps:

S1:采用相差显微镜拍摄需要计数的干细胞,生成干细胞图像;S1: Use a phase-contrast microscope to photograph stem cells that need to be counted, and generate stem cell images;

S2:通过细胞分割技术对干细胞图像进行分割,得到潜在的多个候选干细胞图像;具体地,S2包括如下步骤:S2: Segment the stem cell image by cell segmentation technology to obtain multiple potential candidate stem cell images; specifically, S2 includes the following steps:

S21:图像预处理;对干细胞图像进行降噪和光照均衡化得到光照均匀的干细胞图像;避免因为噪音和光照不均匀而对细胞的准确分割产生影响。S21: image preprocessing; performing noise reduction and illumination equalization on the stem cell image to obtain a stem cell image with uniform illumination; avoiding the impact on the accurate segmentation of cells due to noise and uneven illumination.

本实施例中采用高斯滤波器对干细胞图像进行降噪处理,高斯滤波器被广泛应用于图像处理的降噪过程,将图像中的像素点用本身和领域内的其他像素值加权平均后替换,对于抑制服从正态分布的噪声非常有效。In this embodiment, a Gaussian filter is used to denoise the stem cell image. The Gaussian filter is widely used in the denoising process of image processing, and replaces the pixels in the image with the weighted average of itself and other pixel values in the field. Very effective for suppressing noise that follows a normal distribution.

一维零均值高斯函数为:The one-dimensional zero-mean Gaussian function is:

其中,σ为高斯函数的参数,物理含义为高斯函数的方差,实际使用中需要人为设置,高斯函数的宽度由σ决定。二维零均值离散高斯函数常常被用作平滑滤波器进行图像处理,二维高斯函数为:Among them, σ is the parameter of the Gaussian function, and its physical meaning is the variance of the Gaussian function, which needs to be set manually in actual use, and the width of the Gaussian function is determined by σ. The two-dimensional zero-mean discrete Gaussian function is often used as a smoothing filter for image processing. The two-dimensional Gaussian function is:

然后通过背景减除法对图像进行去光照操作,达到光照均衡的效果。假设背景C为二项多项式模型,即Then, the image is de-illuminated by the background subtraction method to achieve the effect of light balance. Suppose the background C is a binomial polynomial model, namely

C(u,v)=k0+k1u+k2v+k3u2+k4uv+k5v2 C(u,v)=k 0 +k 1 u+k 2 v+k 3 u 2 +k 4 uv+k 5 v 2

其中,u,v为像素点的横纵坐标,k0-k5为该模型的系数。如果想要估算出k,必须知道背景像素gc。若要得到gc,必须分割出背景。为此,首先将所有像素看作背景,再基于最小二乘法估计k的值:Among them, u, v are the horizontal and vertical coordinates of the pixel, and k0-k5 are the coefficients of the model. If we want to estimate k, we must know the background pixel g c . To get g c , the background must be segmented. To do this, first consider all pixels as the background, and then estimate the value of k based on the least squares method:

k*=(ATA)-1ATgk * =(A T A) -1 A T g

其中,A为图像矩阵,获得复原图和分割结果以后,可以通过反复迭代完善背景估计值,对一张图片计算背景C=Ak*,再根据g←g-C将背景去除,得到光照均衡的干细胞图像。Among them, A is the image matrix. After obtaining the restoration image and the segmentation result, the estimated value of the background can be improved through repeated iterations, and the background C=Ak * can be calculated for a picture, and then the background can be removed according to g←gC to obtain a stem cell image with balanced illumination .

S22:去除相差显微镜拍摄过程中产生的细胞伪影;首先,伪影产生的原因与相差显微镜的成像原理有关。培养皿中的细胞,由于未经过染色,基本上是接近完全透明的,使用普通的光学显微手段,直接通过肉眼观察或者拍摄几乎无法得到有效的图像信息,因此,需要一种特殊的光学仪器:相差显微镜。相差显微镜是在普通光学显微镜中加装了环状光栅和相位板两个特殊装置构成的,由于细胞有一定厚度,当光线通过细胞时,会产生相位差,相差显微镜能把这个相位差转换为人眼可见的振幅差(光强度差),拍摄过程中,会在细胞周围产生光圈状细胞伪影,对分割工作造成干扰。S22: Remove the cell artifacts generated during the phase-contrast microscope shooting; first, the cause of the artifacts is related to the imaging principle of the phase-contrast microscope. The cells in the culture dish are almost completely transparent because they have not been dyed. It is almost impossible to obtain effective image information by direct observation or shooting with the naked eye using ordinary optical microscopy methods. Therefore, a special optical instrument is required. : phase contrast microscopy. The phase contrast microscope is composed of two special devices, the annular grating and the phase plate, which are added to the ordinary optical microscope. Since the cells have a certain thickness, when the light passes through the cells, a phase difference will be generated. The phase contrast microscope can convert this phase difference into human The amplitude difference (light intensity difference) visible to the eye will produce halo-shaped cell artifacts around the cells during the shooting process, which will interfere with the segmentation work.

去除细胞伪影的具体方式步骤为:The specific steps for removing cell artifacts are:

设g为成像图像,f为原始图像,H为成像矩阵,C为背景模型,根据相差显微镜成像原理,将成像过程定义为一个线性模型:Suppose g is the imaging image, f is the original image, H is the imaging matrix, and C is the background model. According to the imaging principle of the phase contrast microscope, the imaging process is defined as a linear model:

g≈Hf+Cg≈Hf+C

若背景已去除,则模型可以简化为:If the background has been removed, the model can be simplified as:

g≈Hfg≈Hf

去伪影的过程就是从g恢复f的过程,使用一个约束二次函数来对f进行恢复:The process of removing artifacts is the process of restoring f from g, using a constrained quadratic function to restore f:

其中,L是定义空间像素邻居相似度的拉普拉斯矩阵,Λ是一个正对角矩阵,ws、wr是通过网格搜索学习得到权重不同的正则化项;Among them, L is the Laplacian matrix that defines the similarity of neighbors of spatial pixels, Λ is a positive diagonal matrix, w s and w r are regularization terms with different weights obtained through grid search learning;

上述公式没有封闭解,只能数值逼近,因此约束恢复函数f有非负解,将公式转化为求优化问题:The above formula has no closed solution and can only be approximated numerically. Therefore, the constraint restoration function f has a non-negative solution, and the formula can be transformed into an optimization problem:

O(f)=fTQf+2bTf+c s.t.f≥0O(f)=f T Qf+2b T f+c stf≥0

Q=HTH+wsL+wtQ=H T H+w s L+w t

b=-HTg-wtTf(t)+wrdiag(Λ)/2b=-H T gw tT f (t) +w r diag(Λ)/2

使用非负乘法更新算法得到f,得到去除伪影后的细胞图像,图1为原始图像,图2为去除伪影后的图像。Use the non-negative multiplication update algorithm to obtain f, and obtain the cell image after artifact removal. Figure 1 is the original image, and Figure 2 is the image after artifact removal.

S23:对去除伪影后的干细胞图像进行分割获取候选干细胞图像;S23: Segment the stem cell image after artifact removal to obtain candidate stem cell images;

S23的具体过程为:The specific process of S23 is:

S231:对去除伪影后的干细胞图像进行二值化处理,得到边缘清晰的干细胞图像;S231: Perform binarization processing on the stem cell image after artifact removal to obtain a stem cell image with clear edges;

S232:再计算干细胞图像中每个连通域的面积;S232: Recalculate the area of each connected domain in the stem cell image;

S233;舍弃面积低于阈值的部分,剩下的作为候选细胞区域,阈值根据干细胞图像分辨率来设定,本实施例中,设定最小面积阈值为10像素;S233: Discard the part whose area is lower than the threshold, and use the rest as the candidate cell area, and the threshold is set according to the resolution of the stem cell image. In this embodiment, the minimum area threshold is set to 10 pixels;

S234:对候选细胞区域进行切割,得到候选干细胞图像。S234: Cutting candidate cell regions to obtain candidate stem cell images.

在描述步骤S2前,先对卷积神经网络解释,卷积神经网络CNN已成为当前语音识别、图像识别、视频分析等人工智能领域的研究热点,具有适用性强、特征提取与分类同时进行、全局优化训练参数少等,成为当前机器学习领域的研究热点。卷积神经网络的基本网络结构可以分为五个部分:输入层、特征提取层(卷积层)、特征映射层(下采样层层)、全链接层以及输出层。特征提取层的每个神经元与上层对应的局部感受域相连,通过滤波器和非线性变换来提取局部感受域的特征。下采样层对特征向量进行降维,同时增加模型的抗畸变能力。简单的卷积神经网络结构由两个卷积层(C1,C3)和两个下采样层(S2,S4)交替组成,如图3所示。卷积神经网络中为了降低参数数目,以降低系统复杂度,本发明采用了局部感知原理,理由如下:对于一张1000×1000像素的图像,需包含100万个隐层神经元,如果采用全连接结构,则有1000×1000×1000000=1012个连接,对应1012个权值参数,若采用局部连接结构,设局部感受域为10×10,隐层每个感受域只需和这10×10的局部图像相连接,则只需108个参数,可降低4个数量级,这就可显著简化训练过程。全连接和局部连接结构的区别如图4所示。Before describing step S2, first explain the convolutional neural network. The convolutional neural network CNN has become a research hotspot in the field of artificial intelligence such as speech recognition, image recognition, and video analysis. It has strong applicability, feature extraction and classification at the same time, Global optimization with fewer training parameters has become a research hotspot in the field of machine learning. The basic network structure of convolutional neural network can be divided into five parts: input layer, feature extraction layer (convolutional layer), feature mapping layer (downsampling layer), full connection layer and output layer. Each neuron in the feature extraction layer is connected to the corresponding local receptive field of the upper layer, and the features of the local receptive field are extracted through filters and nonlinear transformations. The downsampling layer reduces the dimensionality of the feature vector while increasing the anti-distortion ability of the model. A simple convolutional neural network structure consists of two convolutional layers (C 1 , C 3 ) and two downsampling layers (S 2 , S 4 ) alternately, as shown in Figure 3. In order to reduce the number of parameters in the convolutional neural network and reduce the complexity of the system, the present invention adopts the principle of local perception for the following reasons: for an image of 1000×1000 pixels, it needs to contain 1 million hidden layer neurons. Connection structure, there are 1000×1000×1000000= 1012 connections, corresponding to 1012 weight parameters, if the local connection structure is used, the local receptive field is set to 10×10, and each receptive field of the hidden layer only needs to be compared with these 10 ×10 local images are connected, only 10 8 parameters are needed, which can be reduced by 4 orders of magnitude, which can significantly simplify the training process. The difference between fully connected and partially connected structures is shown in Figure 4.

除了采用局部连接结构减少参数以外,还可以通过权值共享来减少训练参数的个数。权值共享,即同一个特征映射上的神经元使用相同的权值参数,权值共享的原理图如图5所示。假设,在局部连接网络中,每个神经元都连接10×10的图像区域,即每个神经元都对应100个连接参数。若将这100个连接参数设置成同一个,相当于让每个神经元采用同一个卷积核进行图像卷积,则只需100个参数就可以覆盖整个图像可视域,在保证信息有效性的前提下大幅度减少参数数量。In addition to using local connection structure to reduce parameters, weight sharing can also be used to reduce the number of training parameters. Weight sharing, that is, neurons on the same feature map use the same weight parameters. The schematic diagram of weight sharing is shown in Figure 5. Suppose, in the local connection network, each neuron is connected to a 10×10 image area, that is, each neuron corresponds to 100 connection parameters. If these 100 connection parameters are set to be the same, it is equivalent to letting each neuron use the same convolution kernel for image convolution, then only 100 parameters can cover the entire image visual field, ensuring the validity of information Under the premise of greatly reducing the number of parameters.

接下来,继续上文的步骤描述:Next, continue with the step description above:

S3:细胞识别;具体地,S2包括如下步骤:S3: cell identification; specifically, S2 includes the following steps:

S31:对分割出的干细胞图像进行手工标记,建立训练集;S31的具体步骤为:S31: Manually mark the segmented stem cell images to establish a training set; the specific steps of S31 are:

S311:将一部分分割出的干细胞图像设置分类标签,分为细胞和非细胞两类;S311: Set classification labels on part of the segmented stem cell images, and divide them into two types: cells and non-cells;

S312:以标签为细胞的样本作为正样本,以标签为非细胞的样本作为负样本,对正样本进行旋转扩充,对每张图片进行90°、180°、270°旋转;S312: Take the samples labeled as cells as positive samples, and use the samples labeled as non-cells as negative samples, rotate and expand the positive samples, and rotate each picture by 90°, 180°, and 270°;

S313:对正样本中每一个原图像和旋转后的图像进行镜面转换,将一张正样本图像扩充为8张,将正样本及其扩充图像以及负样本共同组成训练集。S313: Perform mirror transformation on each original image and rotated image in the positive sample, expand one positive sample image into 8 images, and form the training set together with the positive sample, its expanded image and the negative sample.

S32:将训练集输入CNN进行训练;如图6所示S22的具体步骤为:S32: Input the training set into CNN for training; as shown in Figure 6, the specific steps of S22 are:

S321:输入数据为24×24像素点构成的矩阵,第一个特征图层采用5×5的窗口对输入图像进行卷积,得到每个特征图的尺寸为20×20;S321: The input data is a matrix composed of 24×24 pixels, the first feature layer uses a 5×5 window to convolve the input image, and the size of each feature map is obtained as 20×20;

S322:在第一个下采样层对卷积得到的特征图进行下采样操作,得到同样数量的特征图,尺寸减小为10×10;S322: Perform a downsampling operation on the feature map obtained by convolution in the first downsampling layer to obtain the same number of feature maps with a size reduced to 10×10;

S323:再进行一次卷积操作和下采样操作,最终得到一个1×2的输出结果,即候选图像是细胞或者非细胞;S323: Carry out another convolution operation and downsampling operation, and finally obtain a 1×2 output result, that is, the candidate image is a cell or a non-cell;

S324:将输出结果与训练集中对应的标签进行对比,将对比结果(误差)反馈回CNN,使用反向传播算法对CNN中神经元的权值进行更新,重复这个过程,识别的正确率会逐渐升高直到相对稳定,最后得到一个可用于候选图像分类的CNN。S324: Compare the output result with the corresponding label in the training set, feed back the comparison result (error) to the CNN, use the backpropagation algorithm to update the weights of the neurons in the CNN, repeat this process, and the recognition accuracy will gradually increase Increase until relatively stable, and finally get a CNN that can be used for candidate image classification.

S33:统计细胞计数结果。将一张显微图像的所有候选干细胞图像输入CNN进行分类,对每一张候选干细胞图像,CNN都会输出一个结果表示其是否是细胞,对其进行统计得到细胞数量。S33: Statistical cell counting results. Input all candidate stem cell images of a microscopic image into CNN for classification. For each candidate stem cell image, CNN will output a result indicating whether it is a cell, and count it to obtain the number of cells.

采用本实施例的方法后,针对中科院生物医药与健康研究院提供的干细胞图像进行了实验,结果如下表所示:After adopting the method of this embodiment, experiments were carried out on the stem cell images provided by the Institute of Biomedicine and Health, Chinese Academy of Sciences, and the results are shown in the following table:

图片编号picture number 人工计数manual counting 候选细胞数number of candidate cells 冗余率%Redundancy rate% CNN计数CNN count 正确率%Correct rate% 200200 1515 1414 93.3393.33 1414 93.3393.33 250250 3737 3939 105.41105.41 3636 97.3097.30 300300 8181 9090 111.11111.11 7777 95.0695.06 350350 138138 151151 109.42109.42 130130 94.2094.20

本发明彻底解决了传统人工细胞计数消耗大量人力的缺点,也克服了流式计数法会破坏细胞生长环境的缺陷,通过拍摄的细胞图像进行计数,具有稳定、高效、自动、无损等特点,从上表可以看出,准确率较高。The invention completely solves the shortcomings of the traditional manual cell counting that consumes a lot of manpower, and also overcomes the defect that the flow counting method will destroy the cell growth environment, and counts through the captured cell images, which has the characteristics of stability, high efficiency, automatic, non-destructive, etc., from As can be seen from the above table, the accuracy rate is relatively high.

本发明不局限于上述具体实施例,应当理解,本领域的普通技术人员无需创造性劳动就可以根据本发明的构思做出诸多修改和变化。总之,凡本技术领域中技术人员依本发明的构思在现有技术的基础上通过逻辑分析、推理或者有限的实验可以得到的技术方案,皆应在由权利要求书所确定的保护范围内。The present invention is not limited to the above specific embodiments, and it should be understood that those skilled in the art can make many modifications and changes according to the concept of the present invention without creative work. In short, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art shall be within the scope of protection defined by the claims.

Claims (7)

1. the stem cell automatic counting method based on deep learning, it is characterised in that comprise the following steps:
S1:The cell for needing to count is shot using phase contrast microscope, stem cell image is generated;
S2:Stem cell image is split by cell segmentation technology, potential multiple candidate stem cells images are obtained;Specifically Ground, S2 comprises the following steps:
S21:Image preprocessing;Noise reduction process is carried out to stem cell image and illumination equalization processing obtains the dry thin of uniform illumination Born of the same parents' image;
S22:Remove the cell artifact produced in phase contrast microscope shooting process;
S23:The stem cell image for removing pseudo- movie queen is split, multiple candidate stem cells images are obtained;
S3:Cell recognition;Specifically, S3 comprises the following steps:
S31:Manual markings are carried out to the multiple candidate stem cells images being partitioned into, training set is set up;
S32:Training set input CNN is trained, to each candidate stem cells image, CNN can export a result table Whether show it is cell;
S33:Stem cell population in all multiple candidate stem cells images of statistics, draws stem cell count result.
2. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that right in S21 The noise reduction process of stem cell image uses Gaussian filter, and illumination equalization processing uses background subtraction method.
3. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that S22 tool Body process is:
If g is image, f is original image, and H is imaging array, and C is background model, according to phase contrast microscope image-forming principle, Imaging process is defined as a linear model:
g≈Hf+C
If background has been removed, model can be reduced to:
g≈Hf
The process for going artifact is exactly to recover f process from g, and f is recovered using a constraint quadratic function:
<mrow> <mi>O</mi> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>|</mo> <mo>|</mo> <mi>H</mi> <mi>f</mi> <mo>-</mo> <mi>g</mi> <mo>|</mo> <msubsup> <mo>|</mo> <mn>2</mn> <mn>2</mn> </msubsup> <mo>+</mo> <msub> <mi>w</mi> <mi>s</mi> </msub> <msup> <mi>f</mi> <mi>T</mi> </msup> <mi>L</mi> <mi>f</mi> <mo>+</mo> <msub> <mi>w</mi> <mi>r</mi> </msub> <mo>|</mo> <mo>|</mo> <mi>&amp;Lambda;</mi> <mi>f</mi> <mo>|</mo> <msub> <mo>|</mo> <mn>1</mn> </msub> </mrow>
Wherein, L is the Laplacian Matrix of definition space pixel neighbours' similarity, and Λ is a Positive diagonal matrix, ws、wrBe The lower weight factor for learning to obtain by grid search of different regularization rules (takes w in testing hereins=1, wr=0.01);
Above-mentioned formula be not closed solution, can only numerical radius, therefore constraint reconstruction f have non-negative solution, formula is converted into and asked Optimization problem:
O (f)=fTQf+2bTf+c s.t.f≥0
Q=HTH+wsL+wt
B=-HTg-wtTf(t)+wrdiag(Λ)/2
Wherein, c is constant term, wt(w is taken for the weighted factor of time consistency regularization in testing hereint=0.1), f(t)For t Moment corresponding f;F is obtained using non-negative multiplication more new algorithm, obtains removing the stem cell image of pseudo- movie queen.
4. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that S23 tool Body process is:
S231:Binary conversion treatment is carried out to the stem cell image for removing pseudo- movie queen, sharp-edged stem cell image is obtained;
S232:The area of each connected domain in stem cell image is calculated again;
S233;Give up the part that area is less than threshold value, remaining as candidate cell region, threshold value is differentiated according to figure cell image Rate is set;
S234:Candidate cell region is cut, candidate stem cells image is obtained.
5. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that S31 tool Body step is:
S311:The stem cell image that a part is partitioned into sets tag along sort, is divided into cell and the class of acellular two;
S312:To be categorized as the sample of cell as positive sample, to be categorized as the sample of acellular as negative sample, to positive sample Rotation expansion is carried out, every pictures are carried out with 90 °, 180 °, 270 ° of rotations;
S313:Minute surface conversion is carried out to each original image in positive sample and postrotational image, a positive sample image is expanded Fill for 8, positive sample and its expansion image and negative sample are collectively constituted into training set.
6. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that S32 tool Body step is:
S321:Input data is the matrix that 24 × 24 pixels are constituted, and first feature figure layer is using 5 × 5 window to input Image carries out convolution, and the size for obtaining each characteristic pattern is 20 × 20;
S322:The characteristic pattern obtained in first down-sampling layer to convolution carries out down-sampling operation, obtains same amount of feature Figure, size is reduced to 10 × 10;
S323:A convolution operation and down-sampling operation are carried out again, finally give the output result of one 1 × 2, i.e. candidate image It is cell or acellular;
S324:Output result label corresponding with training set is contrasted, comparing result CNN is fed back into, using reverse Propagation algorithm is updated to the weights of neuron in CNN, repeats this process, and the accuracy of identification can be gradually risen until phase To stable, a CNN that can be used for candidate image to classify is finally obtained.
7. the stem cell automatic counting method according to claim 1 based on deep learning, it is characterised in that S33 tool Body process is:All candidate cell images input CNN of one micro-image is classified, to each candidate image, CNN A result will be exported and represent whether it is stem cell, all candidate images for being determined as cell image by CNN are then counted Quantity, obtains inputting the stem cell sum of micro-image.
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