CN1553166A - Microscopic multispectral bone marrow and peripheral blood cell automatic analyzer and method - Google Patents
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Abstract
本发明公开了一种显微多光谱骨髓及外周血细胞自动分析仪和方法,它包括光源、显微镜、显示器及面阵CCD和计算机;其特征在于:a.有一个滤光系统,其核心部件是电调谐滤光器,它与电调谐滤光器控制器和面阵CCD配置在显微镜上,用于实现显微光谱图像的快速采集,计算机则通过控制电调谐滤光器控制器调节加在电调谐滤光器上的电压,改变其通频带的中心频率;b.有一个CCD控制和数据采集系统,用于实现将面阵CCD检测出的图像信号通过图像采集卡转换成数字图像存贮在计算机中,供计算机进行图像处理和分析;c.有一个三维移动控制系统,用于在计算机控制下实现自动聚焦和扫描,自动载物台通过三维移动控制系统与计算机连接,由计算机通过面阵CCD采集的图像计算聚焦、控制自动载物台上下移动进行自动聚焦、水平移动实现涂片扫描;d.还有计算机,用于对三维移动控制系统的控制和对采集到的光谱图像进行图像合成、图像校正、自动分割、自动分类识别。本发明自动分析仪和方法自动化程度高,分析结果准确,可靠性强,充分利用显微光谱对血细胞进行光谱识别和分类,为血液病的诊断提供新的光谱学依据。
The invention discloses a microscopic multispectral bone marrow and peripheral blood cell automatic analyzer and method, which includes a light source, a microscope, a display, an area array CCD and a computer; it is characterized in that: a. There is a filter system, and its core component is The electric tuning filter, which is configured on the microscope with the electric tuning filter controller and the area array CCD, is used to realize the rapid collection of microspectral images, and the computer controls the electric tuning filter controller to adjust the electric Tuning the voltage on the filter to change the center frequency of its passband; b. There is a CCD control and data acquisition system, which is used to convert the image signal detected by the area array CCD into a digital image through the image acquisition card and store it in the In the computer, the computer is used for image processing and analysis; c. There is a three-dimensional mobile control system for automatic focusing and scanning under the control of the computer. The automatic stage is connected to the computer through the three-dimensional mobile control system, and the computer passes through the area array The image collected by the CCD is used to calculate focus, control the automatic stage to move up and down for automatic focus, and move horizontally to realize smear scanning; d. There is also a computer, which is used to control the three-dimensional mobile control system and perform image synthesis on the collected spectral images , Image correction, automatic segmentation, automatic classification and recognition. The automatic analyzer and method of the present invention have high automation degree, accurate analysis result and high reliability, fully utilize microscopic spectrum to carry out spectral identification and classification of blood cells, and provide new spectral basis for diagnosis of blood diseases.
Description
技术领域technical field
本发明涉及一种显微多光谱骨髓及外周血细胞自动分析仪和方法,属于生物医疗检测分析仪器和方法技术领域,特别是涉及人类血液细胞的快速检测分析仪和方法。The invention relates to a microscopic multispectral automatic analyzer and method for bone marrow and peripheral blood cells, belonging to the technical field of biomedical detection and analysis instruments and methods, in particular to a rapid detection analyzer and method for human blood cells.
背景技术Background technique
血液病是严重威胁人类健康的重大疾病之一,不同的血液病,其患者的血液细胞有不同的质(包括形态)和量的变化,常规骨髓(血)细胞瑞氏染色在光镜下人工识别分类存在费时(2小时/例左右),主观性强(不同认识水平的人,或同一人不同时间观察分类结果不一样)。不利于对患者的准确诊治,因此用智能化血液细胞分析仪和方法代替人工分类是医学领域的重要课题。Blood disease is one of the major diseases that seriously threaten human health. Different blood diseases have different qualitative (including shape) and quantitative changes in blood cells of patients. Wright's staining of conventional bone marrow (blood) cells is artificially performed under a light microscope. Recognition and classification are time-consuming (about 2 hours/case) and highly subjective (people with different levels of knowledge, or the same person observes and classifies differently at different times). It is not conducive to accurate diagnosis and treatment of patients, so it is an important topic in the medical field to replace manual classification with intelligent blood cell analyzers and methods.
目前,在血液病诊断中所用的手段主要分为两种:一种是近十年来得到广泛应用的,根据电阻变化、激光散射、射频、组织化学和流式细胞仪诸原理设计制造的自动血液分析仪和方法(AHA)。该方法简单、快速而且可进行多参数测定,但是该类仪器主要根据血细胞的体积大小进行分类,对各种细胞的具体形态、结构特点不能识别,可利用信息较少,一般只能实现三分类或五分类。准确地说应该叫分群,而不叫分类。医学系统的许多研究表明,该类仪器分类对白血病及各种贫血病的异常细胞无能为力,掩盖了许多有效疾病诊断信息。即使是较高档次的自动血液分析仪和方法也仅能作为正常标本的筛选。若不与涂片镜检相配合,很容易产生漏诊或误诊。另一种是通过骨髓细胞涂片染色,然后在显微镜下人工操纵手工机械移动器,用肉眼观察寻找血液细胞,对血细胞的种类及其个数进行观察、判断,这种逐个查看细胞的检查工作是极为烦琐和劳累,需要很多的人力,而且需要专门的医学人员才能识别;由于有些细胞变化差异很小,而且缺乏客观的判定尺度,主观误差无法避免,同时也无法形成有效的定量描述结果。在集体普查逐步开展的今天,传统的检查方法已经不适应需要,人们需要能够自动地完成这些工作的仪器,即用计算机来实现血液细胞的自动识别的方法。At present, the methods used in the diagnosis of blood diseases are mainly divided into two types: one is the automatic hematology device designed and manufactured according to the principles of resistance change, laser scattering, radio frequency, histochemistry and flow cytometry, which has been widely used in the past ten years. Analyzers and Methods (AHA). This method is simple, fast and can measure multiple parameters. However, this type of instrument mainly classifies blood cells according to the volume and size of blood cells. It cannot identify the specific shape and structural characteristics of various cells, and there is less available information. Generally, only three classifications can be achieved. or five categories. To be precise, it should be called grouping, not classification. Many studies in the medical system have shown that this type of instrument classification is ineffective for abnormal cells of leukemia and various anemias, covering up many effective disease diagnosis information. Even higher-grade automatic hematology analyzers and methods can only be used as screening for normal specimens. If it is not coordinated with smear microscopy, it is easy to miss or misdiagnose. The other is to stain the smear of bone marrow cells, then manually manipulate the manual mechanical mover under the microscope, observe and find blood cells with the naked eye, and observe and judge the type and number of blood cells. This kind of inspection work is to check cells one by one It is extremely cumbersome and tiring, requires a lot of manpower, and requires specialized medical personnel to identify; due to the small differences in some cell changes and the lack of objective judgment scales, subjective errors cannot be avoided, and effective quantitative description results cannot be formed. Today, when collective censuses are gradually carried out, traditional inspection methods are no longer suitable for the needs, and people need instruments that can automatically complete these tasks, that is, a method for automatic identification of blood cells by computer.
国际上此类仪器最近的发展是第一种方式的仪器综合两种乃至多种信号来提高分类质量,如Coulter公司的VCS自动白细胞分析仪和方法综合了体积分析、高频传导分析和激光散射分析三种技术。研究表明它确实有助于鉴别诊断部分血液病,但不能作骨髓血细胞分类。第二种方式的发展是采用了自动图像分析仪和方法器,通过计算机提取血细胞和胞核的形态和纹理参数,利用人工智能模仿人眼对细胞进行自动分类。美国和以色列等国均推出了多种商品化的设备,价格在40万美元左右。国内从上世纪八十年代开始从事细胞诊断方面的研究,成立了癌细胞自动识别研究协作组,取得了一些初步成果。九十年代初,东南大学罗立民等利用微机和单色面阵CCD采集白细胞图像,较系统的研究了白细胞自动识别问题,但由于采用单色灰度图像,使细胞分割特征描述方法存在一定难度,导致识别准确率不太理想,其后,随着计算机技术和彩色面阵CCD成像技术的发展,汪定慧、张勇等人将彩色图像处理和分析技术应用于血液细胞自动分类研究,使得系统识别率得到提高,但是目前这些研究成果并未能得到实际应用。The recent development of this type of instrument in the world is the first way of combining two or more signals to improve the quality of classification. For example, Coulter's VCS automatic white blood cell analyzer and method combine volume analysis, high-frequency conduction analysis and laser scattering. Three techniques are analyzed. Studies have shown that it does help in the differential diagnosis of some blood diseases, but it cannot be used for the classification of bone marrow blood cells. The development of the second way is to use an automatic image analyzer and method to extract the shape and texture parameters of blood cells and nuclei by computer, and use artificial intelligence to automatically classify cells by imitating human eyes. Countries such as the United States and Israel have launched a variety of commercialized equipment, with a price of about 400,000 US dollars. Since the 1980s, the country has been engaged in the research of cell diagnosis, established a cancer cell automatic identification research collaboration group, and achieved some preliminary results. In the early 1990s, Luo Limin of Southeast University used a microcomputer and a monochrome area array CCD to collect white blood cell images, and systematically studied the problem of white blood cell automatic identification. However, due to the use of monochrome grayscale images, the cell segmentation feature description method is difficult. , resulting in unsatisfactory recognition accuracy. Later, with the development of computer technology and color area array CCD imaging technology, Wang Dinghui, Zhang Yong and others applied color image processing and analysis technology to the automatic classification of blood cells, making the system recognize The rate has been improved, but these research results have not been practically applied.
第一种方式利用了细胞的物理化学组成信息和细胞大小信息,但却无法像血液病医生一样利用细胞丰富的形态信息;第二种利用细胞的形态和纹理信息,却无法利用细胞本身的成分变化信息。而且第一种方式只适用于外周血检查,不适合于骨髓血细胞。因此目前都无法达到代替人工骨髓(血)细胞分类,只能作为一种过筛手段。使用这类仪器,病人涂片可复检率在30-50%之间。The first method uses the physicochemical composition information and cell size information of cells, but it cannot use the rich morphological information of cells like hematologists; the second method uses the shape and texture information of cells, but cannot use the components of the cells themselves change information. And the first method is only suitable for peripheral blood examination, not for bone marrow blood cells. Therefore, it is currently impossible to replace artificial bone marrow (blood) cell classification, and it can only be used as a screening method. Using this type of instrument, the retest rate of patient smears is between 30-50%.
光谱成像技术的出现,第一次从根本上打破了这种局面。它使我们在获得骨髓(血)细胞形态的同时又能获得骨髓(血)细胞的光谱图,使我们能将骨髓(血)细胞的形态和成分信息同时放到一起进行分析。光谱成像技术最早应用于遥感分析(又称多光谱成像或超谱成像),它使得我们在得到地面照片的同时能根据光谱信息识别出森林、麦田、草地,乃至是地表下面的矿藏。用于显微光谱成像的器件从98年以后逐渐走向成熟。国外众多的研究工作者正在逐步改进器件的性能和扩大应用领域。The emergence of spectral imaging technology fundamentally broke this situation for the first time. It allows us to obtain the spectrogram of bone marrow (blood) cells while obtaining the morphology of bone marrow (blood) cells, so that we can analyze the shape and composition information of bone marrow (blood) cells at the same time. Spectral imaging technology was first applied to remote sensing analysis (also known as multispectral imaging or hyperspectral imaging), which allows us to identify forests, wheat fields, grasslands, and even mineral deposits under the surface based on spectral information while obtaining ground photos. Devices for microspectral imaging have gradually matured since 1998. Many foreign researchers are gradually improving the performance of the device and expanding the application field.
生物医学上的多光谱成像技术可用来加大获取的信息,加强计算机对细胞的识别能力。从原理上讲,当正常细胞的性质发生改变时,其细胞核中的一些物质,如脱氧核糖核酸(DNA)的含量亦随之而变,导致细胞核的形态、大小及核浆比例发生变化,并且变化的类型根据细胞的种类及恶性程度而有所不同;因而对一般细胞图像灰度和形态学的分析,最多只能得到细胞60%-70%的识别信息;远不如多光谱图像在观察细胞形态的同时,通过成像光谱分析,得到细胞中组成变化的信息(被吸收光的波长直接与吸收光的物质成分有关),这些细胞化学成分定量分析信息,使得原有的计算机图像分析方法有了很广泛的应用前景。国内一些学者用光纤光谱仪在显微镜上研究了人体血液白细胞光谱,他们注意到了患者的淋巴细胞和粒细胞与正常人的相比具有明显不同的光谱特性,而且不同类型的患者的同一种自细胞的光谱也不相同。由此可见,利用显微光谱能对白细胞进行光谱识别和分类,可为白血病诊断提供新的光谱学依据。Multispectral imaging technology in biomedicine can be used to increase the information obtained and enhance the computer's ability to identify cells. In principle, when the nature of normal cells changes, the content of some substances in the nucleus, such as deoxyribonucleic acid (DNA), also changes, resulting in changes in the shape, size and ratio of nucleoplasm of the nucleus, and The type of change varies according to the type of cell and the degree of malignancy; therefore, the analysis of the grayscale and morphology of general cell images can only obtain 60%-70% of the identification information of cells at most; it is far less effective than multispectral images in observing cells. At the same time, through the analysis of imaging spectrum, the information of the composition change in the cell is obtained (the wavelength of the absorbed light is directly related to the material composition of the absorbed light), and the quantitative analysis information of the chemical composition of these cells makes the original computer image analysis method have Very broad application prospects. Some domestic scholars have used fiber optic spectrometers to study the spectrum of human blood leukocytes on a microscope. They have noticed that lymphocytes and granulocytes in patients have significantly different spectral characteristics compared with normal people, and the same kind of self-cells in different types of patients The spectrum is also not the same. It can be seen that the spectral identification and classification of white blood cells can be carried out by using microspectroscopy, which can provide a new spectral basis for the diagnosis of leukemia.
发明内容Contents of the invention
本发明目的是:将多光谱成像技术与显微技术相结合,利用新型的电调谐液晶滤光器(LCTF)代替传统的滤光器实现波长的快速扫描,采用高灵敏度制冷面阵CCD对骨髓(血)细胞瑞氏染色涂片图像进行数字化。它在获得骨髓(血)细胞形态的同时,又获得了骨髓(血)细胞的光谱图。利用光谱成像技术系统的研究正常人骨髓(血)涂片中不同血细胞的特征(同种细胞的共性,不同细胞的差异性,以及制样、光源等外界因素对光谱的影响及消除方法),发生病变后细胞的光谱特性的变化,血细胞的形态参数特性等,为血液病的早期诊断和自动化诊断研究打下坚实的基础。通过成像光谱分析,可以得到细胞中组成变化的信息,从而使对未病变细胞和病变细胞的分析识别更加准确快速。与国际上目前只利用细胞形态的血液自动图像分析仪和方法相比,本仪器自动化程度以及分析结果的准确性和可靠性都得到了极大的提高。和人工检查相比,除了能减轻复杂手工劳动外,检测准确率将大大提高,将使更多的患者在早期就能发现疾病,达到治愈的目的,其社会效益是巨大的。和国际上目只利用细胞形态的血液自动图像分析仪和方法相比,检测准确率有明显提高。The purpose of the present invention is to combine multi-spectral imaging technology with microscopic technology, use a new type of electronically tuned liquid crystal filter (LCTF) to replace the traditional filter to achieve rapid wavelength scanning, and use a high-sensitivity cooling area array CCD to monitor bone marrow. (Blood) Cellular Wright's stained smear images were digitized. While obtaining the morphology of bone marrow (blood) cells, it also obtains the spectrum of bone marrow (blood) cells. Using spectral imaging technology to systematically study the characteristics of different blood cells in normal human bone marrow (blood) smears (the commonality of the same type of cells, the differences of different cells, and the influence and elimination methods of external factors such as sample preparation and light source on the spectrum), The changes in the spectral characteristics of cells after the lesion occurs, the morphological parameters of blood cells, etc., lay a solid foundation for the early diagnosis and automatic diagnosis of blood diseases. Through imaging spectral analysis, information on compositional changes in cells can be obtained, thereby making the analysis and identification of non-diseased cells and diseased cells more accurate and rapid. Compared with the current international blood automatic image analyzers and methods that only use cell morphology, the degree of automation of the instrument and the accuracy and reliability of the analysis results have been greatly improved. Compared with manual inspection, in addition to reducing complex manual labor, the detection accuracy will be greatly improved, and more patients will be able to detect diseases at an early stage and achieve the purpose of cure. The social benefits are huge. Compared with the international blood automatic image analyzers and methods that only use cell morphology, the detection accuracy is significantly improved.
本发明所涉及的一种显微多光谱骨髓及外周血细胞自动分析仪和方法的技术目的是这样实现的:它包括光源、显微镜、显示器及、面阵CCD和计算机;其特征在于;a、有一个滤光系统,其核心部件是电调谐滤光器,它与电调谐滤光器控制器和面阵CCD配置在显微镜上,用于实现显微光谱图像的快速采集,计算机则通过控制电调谐滤光器控制器调节加在电调谐滤光器上的电压,改变其通频带的中心频率;b、有一个CCD控制和数据采集系统,用于实现将面阵CCD检测出的图像信号通过图像采集卡转换成数字图像存贮在计算机中,供计算机进行图像处理和分析;c、有一个三维移动控制系统,用于在计算机控制下实现自动聚焦和自动扫描,自动载物台通过三维移动控制系统与计算机连接,由计算机通过扫描;d、还有计算机,用于对三维移动控制系统的控制和对采集到的光谱图像进行图像合成、图像校正、自动分割、自动分类识别。面阵CCD采集的图像计算聚焦、控制自动载物台上下移动进行自动聚焦,水平移动实现涂片The technical purpose of a kind of microscopic multi-spectral bone marrow and peripheral blood cell automatic analyzer and method involved in the present invention is achieved in that it comprises light source, microscope, display and, area array CCD and computer; It is characterized in that; a, has A filter system, the core component of which is the electric tuning filter, which is configured on the microscope with the electric tuning filter controller and the area array CCD, is used to realize the rapid collection of microspectral images, and the computer controls the electric tuning The filter controller adjusts the voltage applied to the electric tuning filter to change the center frequency of its passband; b. There is a CCD control and data acquisition system, which is used to realize the image signal detected by the area array CCD through the image The acquisition card is converted into a digital image and stored in the computer for image processing and analysis by the computer; c. There is a three-dimensional movement control system for automatic focusing and automatic scanning under the control of the computer. The automatic stage is controlled by three-dimensional movement The system is connected with a computer, which is scanned by the computer; d, there is also a computer, which is used to control the three-dimensional mobile control system and perform image synthesis, image correction, automatic segmentation, and automatic classification and recognition of the collected spectral images. Calculate the focus of the image collected by the area array CCD, control the automatic stage to move up and down for automatic focus, and move horizontally to realize the smear
本发明的技术效果:与现有技术相比,具有突出的特点和显著的进步。其中,与国际上目前只利用细胞形态的血液自动图像分析仪和方法相比,自动化程度、分析结果准性、可靠性有显著的提高,充分利用显微光谱对血细胞进行光谱识别和分类,为血液病的诊断提供新的光谱学依据;与人工检查相比,除了能减轻复杂手工劳动外,检测准确率大大提高。本发明使更多的患者在早期就能发现疾病,达到治愈的目的,其社会效益巨大。Technical effect of the present invention: Compared with the prior art, it has outstanding features and remarkable progress. Among them, compared with the current international blood automatic image analyzers and methods that only use cell morphology, the degree of automation, accuracy of analysis results, and reliability have been significantly improved. Microscopic spectroscopy is fully used to identify and classify blood cells. The diagnosis of blood diseases provides a new spectroscopic basis; compared with manual inspection, in addition to reducing complex manual labor, the detection accuracy is greatly improved. The invention enables more patients to discover diseases at an early stage and achieve the purpose of curing, and has huge social benefits.
附图说明Description of drawings
图1是整个仪器的结构和方法示意图,其中:1-显微镜 2-光源,3-自动载物台,4-三维移动控制系统 5-电调谐滤光器控制器,6-打印机及其它外设,7-计算机,8-数据备份恢复系统,9-显示器,10-图像监视器,11-CCD控制和数据采集系统,12-面阵CCD,13、电调谐滤光器。Figure 1 is a schematic diagram of the structure and method of the entire instrument, in which: 1-microscope 2-light source, 3-automatic stage, 4-three-dimensional movement control system 5-electrically tuned filter controller, 6-printer and other peripherals , 7-computer, 8-data backup and recovery system, 9-monitor, 10-image monitor, 11-CCD control and data acquisition system, 12-area array CCD, 13, electric tuning filter.
图2是系统工作流程图Figure 2 is a flow chart of the system work
图3是多光谱图像采集流程图Figure 3 is a flow chart of multispectral image acquisition
图4是一幅典型的骨髓图像,Figure 4 is a typical bone marrow image,
图5是图4中矩形区域内一个平均的背底、成熟的红细胞、细胞浆和细胞核的光谱曲线Figure 5 is an average spectral curve of the background, mature red blood cells, cytoplasm and nucleus within the rectangular area in Figure 4
图6是自动分割算法流程图Figure 6 is a flowchart of the automatic segmentation algorithm
具体实施方式Detailed ways
下面结合附图对本发明做进一步说明:The present invention will be further described below in conjunction with accompanying drawing:
在图1是本发明自动分析仪和方法的一个实施例。它有一个光源2、显微镜1、显示器9及面阵CCD12和计算机7;还有:a、有一个滤光系统,其核心部件是电调谐滤光器13,它与电调谐滤光器控制器5和面阵CCD 12配置在显微镜1上,用于实现显微光谱图像的快速采集,计算机7则通过控制电调谐滤光器控制器5调节加在电调谐滤光器13上的电压,改变其通频带的中心频率;b、有一个CCD控制和数据采集系统11,用于实现将面阵CCD12检测出的图像信号通过图像采集卡转换成数字图像存贮在计算机7中,供计算机进行图像处理和分析;c、有一个三维移动控制系统4,用于在计算机控制下实现自动聚焦和自动扫描,自动载物台3通过三维移动控制系统4与计算机7连接,由计算机7通过面阵CCD12采集的图像计算聚焦、控制自动载物台3上下移动进行自动聚焦和水平移动实现涂片扫描;d、还有计算机7,用于对三维移动控制系统(4)的控制和对采集到的光谱图像进行图像合成、图像校正、自动分割、自动分类识别;进一步地说:所述电调谐滤光器13一端通过标准C接口安装在显微镜1上,另一端与面阵CCD12连接,染色涂片细胞像经过电调谐滤光器13后成像在面阵CCD12上;电调谐器件13是安装在面阵CCD12的前方,通过一个用于补偿光路长度镜筒安装在显微镜1上;目标物像通过显微镜放大后,再通过电调谐滤光器13成象在面阵CCD12上;面阵CCD12检测出的图像信号通过面阵CCD的控制和数据采集系统11转换成数字图像存贮在计算机7中,供计算机7进行图像处理和分析;电调谐滤光器13作为滤光器件,它的带宽为5-30nm,可以是液晶调谐滤光器,也可以是其他类型的电调谐滤光器,通过计算机控制加在电调谐滤光器13上的电压,改变透过电调谐滤光器13的光波长;涂片在连续光的照射下,细胞或其它微小物体所产生的光信号(透射光)通过电调谐滤光器13后,成像在面阵CCD12上;在计算机7的控制下,在每个视场下,可在选取的几个波段上每个波段采集一幅图像,然后通过计算机7对所采集到的光谱图像进行图像合成、图像校正、自动分割、自动分类识别等操作;光源2采用科勒照明,保证视场的均匀性;在不同的放大倍数下采用不同的面阵CCD12曝光时间以及不同的光源强度,在相同的放大倍数下,采用相同的面阵CCD12曝光时间和相同的光源强度,保证了采集条件的一致性。In Figure 1 is an embodiment of the automated analyzer and method of the present invention. It has a light source 2, microscope 1, display 9 and area array CCD12 and computer 7; Also have: a, have a filter system, its key component is the electric tuning filter 13, and it and electric tuning filter controller 5 and the area array CCD 12 are configured on the microscope 1 to realize the rapid collection of microspectral images, and the computer 7 adjusts the voltage applied to the electric tuning filter 13 by controlling the electric tuning filter controller 5 to change the The central frequency of its passband; b, there is a CCD control and data acquisition system 11, which is used to realize that the image signal detected by the area array CCD12 is converted into a digital image by the image acquisition card and stored in the computer 7, for the computer to carry out the image Processing and analysis; c, there is a three-dimensional mobile control system 4, which is used to realize automatic focusing and automatic scanning under computer control. The automatic stage 3 is connected with the computer 7 through the three-dimensional mobile control system 4, and the computer 7 passes through the area array CCD12 The collected image calculates the focus, controls the automatic stage 3 to move up and down for automatic focus and horizontal movement to realize smear scanning; d, there is also a computer 7, which is used to control the three-dimensional mobile control system (4) and to collect the spectrum Image synthesis, image correction, automatic segmentation, automatic classification and recognition; further speaking: one end of the electric tuning filter 13 is installed on the microscope 1 through a standard C interface, and the other end is connected to the area array CCD12 to stain the smear cells The image is imaged on the area array CCD12 after passing through the electrical tuning filter 13; the electrical tuning device 13 is installed in front of the area array CCD12, and is installed on the microscope 1 through a lens barrel for compensating the optical path length; the target object image is enlarged through the microscope After that, it is imaged on the area array CCD12 through the electric tuning filter 13; the image signal detected by the area array CCD12 is converted into a digital image by the control and data acquisition system 11 of the area array CCD and stored in the computer 7 for the computer 7 for image processing and analysis; the electrical tuning filter 13 is used as a filter device, and its bandwidth is 5-30nm, which can be a liquid crystal tuning filter or other types of electrical tuning filters, which are controlled by a computer The voltage on the electric tuning filter 13 changes the wavelength of light passing through the electric tuning filter 13; the smear is irradiated by continuous light, and the optical signal (transmitted light) generated by cells or other tiny objects is passed through the electric tuning After the optical filter 13, it is imaged on the area array CCD12; under the control of the computer 7, under each field of view, an image can be collected in each of several selected wavebands, and then the collected images are collected by the computer 7. The spectrum images obtained are image synthesis, image correction, automatic segmentation, automatic classification and recognition, etc.; the light source 2 adopts Kohler illumination to ensure the uniformity of the field of view; different area array CCD12 exposure times and different The light source intensity, under the same magnification, adopts the same area array CCD12 exposure time and the same light source intensity to ensure the consistency of the acquisition conditions.
图2是系统工作流程图。Figure 2 is a flow chart of the system work.
打开显微镜1的光源2和电调谐滤光器控制器5,约半小时待光源2稳定后,就可以开始工作了。首先采集背景光谱,方法见后面的多光谱图像采集方法。骨髓(血)涂片放到自动载物台3上后,手动聚焦,调到合适位置;启动计算机7自动扫描程序;在计算机7的控制下,自动移动到一个视场,将电调谐滤光器13的透射波长定位到将要采集的波长的中间波长,在此波长下采集一幅图像,对采集到的图像进行分析,利用图像处理的方法判断玻片是否聚焦,如果没有聚焦,那么在计算机7的控制下不断调节自动载物台3的高度直到聚焦为止;当一个视场聚焦后,就可以采集一个多光谱图像,一个视场的多光谱图像采集完毕后,计算机7自动进行图像分割,找出细胞浆和细胞核;图像经分割完后,可以进行细胞特征测量,提取一些形态特征和光谱特征,用这些特征进行细胞分类,一个视场分割和分析完毕后,再继续采集和分析下一个视场,待采集的视场数目达到一定要求后,停止采集图像,对前面所有采集的细胞进行综合分析,根据在医生指导下建立的疾病数据库利用神经网络分类器得出疾病分类。Turn on the light source 2 and the ETF controller 5 of the microscope 1, and start working after the light source 2 is stable for about half an hour. First, collect the background spectrum. For the method, see the following multispectral image collection method. After the bone marrow (blood) smear is placed on the automatic stage 3, manually focus and adjust to a suitable position; start the automatic scanning program of the computer 7; The transmission wavelength of the device 13 is positioned at the middle wavelength of the wavelength to be collected, an image is collected at this wavelength, the collected image is analyzed, and the image processing method is used to judge whether the slide is in focus, if not, then in the computer Under the control of 7, the height of the automatic stage 3 is continuously adjusted until it is focused; when a field of view is focused, a multispectral image can be collected, and after the multispectral image of a field of view is collected, the computer 7 automatically performs image segmentation. Find the cytoplasm and nucleus; after the image is segmented, you can measure the cell characteristics, extract some morphological features and spectral features, and use these features to classify the cells. After one field of view is segmented and analyzed, continue to collect and analyze the next one. Field of view: After the number of fields of view to be collected reaches a certain requirement, the image collection is stopped, and all previously collected cells are analyzed comprehensively, and the disease classification is obtained by using the neural network classifier according to the disease database established under the guidance of the doctor.
图3是多光谱图像采集流程图。由于对光谱图像是由一系列的单色图像组合而成的,因此,在每个视场下需要在每一个我们选定的波长下采集一幅图像;打开光源2和电调谐滤光器控制器5,约半小时光源稳定后,把骨髓(血)涂片放在自动载物台3上,把光源2调节到一个合适的位置,对于相同的放大倍率以后每次都放到相同的位置,使照明强度相同;调节显微镜1的光源到科勒照明,以保证视场的亮度均匀;图像采集的方法是在每一个视场下,首先将电调谐滤光器13的透射波长定位到我们要采集的波长的中间波长,在计算机7的控制下利用图像处理算法进行自动聚焦,使视场最清晰,然后通过计算机7控制电调谐滤光器5切换到一个选定的波段下,采集一幅图像,对采集到的图像进行灰度校正,然后再切换到下一个选定的波段再采集一幅图像再进行灰度校正,如此循环,直到所有所选的波段全部采集完毕,再进行彩色合成和数据存储,利用计算机7图像处理技术自动找出血细胞的胞核和胞浆,测量出细胞的特征参数,利用神经网络的方法得出细胞所属的类别,连同特征参数一并存入数据库。再采集下一个视场,直到扫描视场数目达到要求;当一个涂片的所有感兴趣的区域都采集完毕以后,就可以对测量的数据进行全自动分析,给出是否病变信息、百分比、良性恶性程度等信息;Figure 3 is a flow chart of multispectral image acquisition. Since the spectral image is composed of a series of monochromatic images, it is necessary to collect an image at each wavelength selected by us in each field of view; turn on the light source 2 and control the electric tuning filter Device 5, after the light source is stable for about half an hour, put the bone marrow (blood) smear on the automatic stage 3, adjust the light source 2 to a suitable position, and put it in the same position every time for the same magnification , so that the illumination intensity is the same; adjust the light source of the microscope 1 to Kohler illumination to ensure that the brightness of the field of view is uniform; the method of image acquisition is to first position the transmission wavelength of the electric tuning filter 13 to the position we want in each field of view. The middle wavelength of the collected wavelength is automatically focused by using an image processing algorithm under the control of the computer 7 to make the field of view the clearest, and then the electronically tuned filter 5 is switched to a selected waveband through the computer 7 to collect a picture Image, perform grayscale correction on the collected image, then switch to the next selected band and then collect an image and then perform grayscale correction, and so on, until all the selected bands are collected, and then perform color synthesis And data storage, use computer 7 image processing technology to automatically find the nucleus and cytoplasm of blood cells, measure the characteristic parameters of the cells, use the neural network method to obtain the category of the cells, and store them in the database together with the characteristic parameters. Then collect the next field of view until the number of scanning fields of view meets the requirements; when all the regions of interest in a smear are collected, the measured data can be automatically analyzed to give whether the lesion information, percentage, benign malignancy and other information;
由于白炽光源在光谱输出中呈现为光谱中的蓝光较低,而近红外(NIR)较高;面阵CCD的量子效率(QE)曲线通常是在蓝色区域很少,而在红光最高。电调谐滤光器13的通量特性(throughputproperties)也表现为一定的光谱曲线。例如,LCTF滤光器在蓝光透过较少,而在NIR部分透过率高些。因此,我们会遇上在蓝光的低性能的综合影响的麻烦。这个问题可以通过选择在蓝色部分具有较高QE的面阵CCD来得到缓解,例如一个薄的背照式(backilluminate)面阵CCD。但是这种情况下就需要一个高性能的相机,这将导致价格的提高。本发明通过软件校正的方法很好的解决了白炽光源的问题,还有效的消除激发光源2、显微镜1、电调谐滤光器13、检测器对成像光谱的影响,保证成像检测的一致性;Since the incandescent light source presents lower blue light in the spectrum and higher near-infrared (NIR) in the spectral output; the quantum efficiency (QE) curve of the area array CCD is usually less in the blue region and the highest in the red light. The throughput properties (throughput properties) of the electrically tuned filter 13 are also shown as certain spectral curves. For example, LCTF filters have less transmittance in blue light and higher transmittance in NIR. Thus, we run into trouble with the combined effect of low performance on Blu-ray. This problem can be alleviated by choosing an area CCD with a higher QE in the blue portion, such as a thin backilluminated area CCD. But in this case, a high-performance camera is required, which will lead to an increase in price. The present invention solves the problem of the incandescent light source well through the method of software correction, and effectively eliminates the influence of the excitation light source 2, the microscope 1, the electric tuning filter 13, and the detector on the imaging spectrum, and ensures the consistency of imaging detection;
灰度校正的方法是;聚焦涂片,使细胞最佳清晰;移动涂片到空白区域,在一个空视场下,略微散焦,通过计算机7控制电调谐滤光器5和面阵CCD12,用一个相同的积分时间在每一个所选定的波段采集一幅图像,再根据这些图像算出每个波段下一个合适的积分时间,对于灰度值比较低的波段可以给一个长的积分时间,对于灰度值高的波段给一个短的积分时间,以对整个波段范围进行平衡补偿。根据计算出的积分时间再在每个选定的波段下重新采集一幅图像,得到增强的背底光谱图像。以后对于每一个波段的图像,都用与增强的背底光谱图像相同的积分时间进行采集,都与增强的背底光谱图像对应的波段图像进行除运算然后再乘以一个比例因子对灰度值进行提升,这样处理后可以保证背底区域在不同的波段都有一致的灰度值,便于图像处理和分析。由于对背底的校正是以象素为单位进行处理的,所以不仅可以有效去除成像系统对不同波段灵敏度不同的影响,也可一对蓝光波段进行补偿提升,有效的消除一些轻微的视场不均匀性以及面阵CCD12各象素感光灵敏度差异。The method of grayscale correction is: focus on the smear to make the cells clear best; move the smear to a blank area, under an empty field of view, slightly defocus, control the electric tuning filter 5 and the area array CCD12 through the computer 7, Use the same integration time to collect an image in each selected band, and then calculate the next appropriate integration time for each band based on these images. A long integration time can be given for the band with a relatively low gray value. Give a short integration time to the band with high gray value to compensate for the balance of the whole band range. According to the calculated integration time, another image is collected under each selected band to obtain an enhanced background spectrum image. In the future, for the image of each band, it will be collected with the same integration time as the enhanced background spectrum image, and the band image corresponding to the enhanced background spectrum image will be divided and then multiplied by a scaling factor to adjust the gray value It can be improved to ensure that the background area has consistent gray values in different bands after processing, which is convenient for image processing and analysis. Since the correction of the background is processed in units of pixels, it can not only effectively remove the influence of the different sensitivity of the imaging system on different bands, but also compensate and improve the blue-ray bands, effectively eliminating some slight field of view inconsistencies. Uniformity and photosensitivity difference of each pixel of area array CCD12.
图4是一幅典型的骨髓图像,图5是图4中矩形区域内的背底、成熟的红细胞、细胞浆和细胞核的平均的光谱曲线。图5的横坐标是波长,纵坐标是透射光的强度,每隔10nm采集一幅图像,每个点代表图4中在每个波段内的单色图像的矩形区域内的平均灰度。Figure 4 is a typical bone marrow image, and Figure 5 is the average spectral curve of the background, mature red blood cells, cytoplasm and nucleus within the rectangular area in Figure 4. The abscissa in Figure 5 is the wavelength, and the ordinate is the intensity of the transmitted light. An image is collected every 10 nm, and each point represents the average gray level in the rectangular area of the monochromatic image in each waveband in Figure 4 .
从图5可以看出,不同的物质,它们的光谱透射率是不一样的。我们可以利用这些差异对它们进行自动分割。由于细胞核、细胞浆和背景区域具有不同的光谱特性,利用它们的光谱差异,采用基于象素的图像分割方法对图像进行分割,取得了良好的分割效果。It can be seen from Figure 5 that different substances have different spectral transmittances. We can use these differences to automatically segment them. Because the nuclei, cytoplasm and background regions have different spectral characteristics, using their spectral differences, the image segmentation method based on pixels is used to segment the image, and a good segmentation effect is obtained.
主要采用的分割方法光谱比值操作。由于不同类型骨髓细胞有的颜色差别较小,而且由于染色深浅也很难完全一致,单纯利用某一个波段的差别或者利用图像的绝对灰度值还很难准确的区分出细胞核、细胞浆和背底。因此,我们采用谱比值的方法来加大它们之间的差异,而且比值是相对值,这就大大减小了染色深浅以及感光单元灵敏度对图像分割结果的影响。具体方法是选择两个波段,对每个图像象素,利用这两个波段的灰度进行相除运算,再把得到的结果乘以一个固定的放大因子,生成一幅新的单色图像,再利用这个单色图像进行自动阈值分割,本发明用的是最大方差阈值法,取得了良好的效果。本发明找到了几组波长,可以分别分割出细胞浆和细胞核;The main segmentation method used is the spectral ratio operation. Because the color difference of different types of bone marrow cells is small, and because the staining depth is difficult to be completely consistent, it is difficult to accurately distinguish the nucleus, cytoplasm and background by simply using the difference in a certain band or the absolute gray value of the image. end. Therefore, we use the spectral ratio method to increase the difference between them, and the ratio is a relative value, which greatly reduces the influence of the dyeing depth and the sensitivity of the photoreceptor unit on the image segmentation results. The specific method is to select two bands, and for each image pixel, use the gray levels of the two bands to perform a division operation, and then multiply the obtained result by a fixed magnification factor to generate a new monochrome image. This monochromatic image is utilized to carry out automatic threshold value segmentation, what the present invention uses is the maximum variance threshold value method, has obtained good effect. The present invention has found several groups of wavelengths, which can separate the cytoplasm and nucleus respectively;
由于光谱图像的海量数据,波段选择是处理光谱图像的一个必要环节,波段选择一方面能有效的减少计算量,另一方面也去除了其中的冗余信息。特征选择的搜索算法可分为最优搜索和次优搜索两类,最优算法有穷举法和分支定界算法;次优算法则包括模拟退火算法、Tabu算法和遗传算法等等。从既能得到最优结果并且实现简单的角度出发,本发明采用穷举法。本发明找波长的方法是用穷举法遍历所有可能的波长组,对应不同的波长组,分别求出这两种物质对应的灰度的比值,构成特征空间,然后求出这两类物质的可分性判据(如类内类间距离,Bhattacharyya距离和散度),比较分类时的错误率,错误率最小的一组波长即为所求。Due to the massive data of spectral images, band selection is a necessary link in processing spectral images. On the one hand, band selection can effectively reduce the amount of calculation, and on the other hand, it can also remove redundant information. The search algorithm of feature selection can be divided into optimal search and suboptimal search. The optimal algorithm includes exhaustive method and branch and bound algorithm; the suboptimal algorithm includes simulated annealing algorithm, Tabu algorithm and genetic algorithm, etc. From the perspective of obtaining the optimal result and simple implementation, the present invention adopts an exhaustive method. The method for finding the wavelength of the present invention is to use the exhaustive method to traverse all possible wavelength groups, corresponding to different wavelength groups, respectively find the ratio of the corresponding gray levels of these two kinds of substances to form a feature space, and then find out the ratio of the two types of substances. Separability criteria (such as intra-class and inter-class distance, Bhattacharyya distance and divergence), compare the error rate of classification, and a group of wavelengths with the smallest error rate is the desired one.
图6是骨髓(血)涂片细胞图像自动分割(检测)流程图Fig. 6 is a flow chart of automatic segmentation (detection) of bone marrow (blood) smear cell image
本发明研究了骨髓(血)涂片细胞图像自动分割(检测)算法,找到了一系列全自动快速分割算法。图像分割主要采用谱比值的方法。由于细胞核、细胞浆、成熟的红细胞以及背底具有不同的光谱特性,因此,可以利用它们的光谱特性进行全自动图像分割。具体分割流程是:选取若干个波段,利用这些波段信息找出空白区域;再选若干波段,利用波段信息找出成熟的红细胞区域;删除红细胞区域和空白背底区域后就可以得到有核细胞区域。再执行腐蚀膨胀等二值形态学操作去除一些小的颗粒,得到区域S1。选择两个波段,进行光谱比值操作,对得到的比值图像,在S1所标记的区域利用最大方差阈值法找到一个全局的阈值,可以把胞浆和胞核分开,得到胞核区域S2,这样分割的效果比较粗略,但速度很快。删除S2中小面积区域,删除的部分用胞浆标记填充,然后再删除没有与胞核相连的区域得到区域S3,那么S3就是一个一个的细胞了。由于前面的分割是一个粗略的分割,因此,再选若干个波段,以每个细胞作为分割单位,利用波段信息对它进行精确分割,可以准确的分割出每个细胞的胞核和胞浆。细胞分割完之后,就可以进行分类识别了。The present invention studies the automatic segmentation (detection) algorithm of bone marrow (blood) smear cell image, and finds out a series of automatic fast segmentation algorithms. Image segmentation mainly uses the spectral ratio method. Since the nuclei, cytoplasm, mature red blood cells, and background have different spectral properties, their spectral properties can be used for fully automatic image segmentation. The specific segmentation process is: select several bands, use the band information to find the blank area; select several bands, use the band information to find the mature red blood cell area; delete the red blood cell area and the blank background area to get the nucleated cell area . Then perform binary morphological operations such as erosion and expansion to remove some small particles, and obtain region S1. Select two bands and perform spectral ratio operation. For the obtained ratio image, use the maximum variance threshold method to find a global threshold in the area marked by S1, which can separate the cytoplasm and nucleus to obtain the nucleus area S2. The effect is rough, but fast. Delete the small area in S2, fill the deleted part with cytoplasmic markers, and then delete the area that is not connected to the nucleus to get the area S3, then S3 is a cell one by one. Since the previous segmentation is a rough segmentation, several bands are selected, and each cell is used as a segmentation unit, and the band information is used to accurately segment it, so that the nucleus and cytoplasm of each cell can be accurately segmented. After the cells are segmented, they can be classified and identified.
利用目视的方法来看图像分割的效果。对于外周血涂片,对Wright染色的约8000个细胞进行分割,单核细胞、淋巴细胞、中性杆状核粒细胞、中性分叶核粒细胞、嗜酸性粒细胞的细胞核分割的准确度在98.7%以上,嗜碱性粒细胞细胞核分割的准确度为90.2%。其细胞浆分割的准确度稍差一点,主要是因为有的细胞浆与其它无核细胞相比,其光谱差异极小,但分割准确率也达到了90%以上,远高于现有文献所报道的值。对于Wright染色的骨髓细胞涂片,对约9000个细胞进行分割,常见的晚幼红细胞、中幼红细胞、早幼红细胞、中性分叶核、中性杆状核、中性中幼粒细胞、中性晚幼粒细胞、淋巴细胞其细胞核分割的准确度在98.5%以上,对于比较少的嗜酸性和嗜碱性细胞,细胞核分割的准确度在95.1%以上,对于胞浆的分割,除了晚幼红以外,准确率均在94.3%以上,由于晚幼红长大以后就变成了成熟的红细胞,所以其胞浆的光谱特性与成熟的红细胞十分接近,故容易出现错分的情况,从目前的情况来看,准确率也达到了85.7%。Use the visual method to see the effect of image segmentation. Accuracy of nucleus segmentation for monocytes, lymphocytes, neutrophil rods, neutrophils, and eosinophils, for peripheral blood smears, for segmentation of approximately 8,000 cells stained by Wright At above 98.7%, the accuracy of basophil nuclei segmentation was 90.2%. The accuracy of the cytoplasmic segmentation is slightly worse, mainly because the spectral difference of some cytoplasmic cells is very small compared with other anucleated cells, but the segmentation accuracy rate has reached more than 90%, which is much higher than the existing literature. reported value. For the bone marrow cell smear stained by Wright, about 9000 cells were divided, common metamyelocytes, myelocytes, promyelocytes, neutral segmented nuclei, neutral rod-shaped nuclei, neutrophil myelocytes, The accuracy of nucleus segmentation in neutrophil metamyelocytes and lymphocytes is above 98.5%. For relatively few eosinophils and basophils, the accuracy of nucleus segmentation is above 95.1%. For the segmentation of cytoplasm, except for late Except for young red blood cells, the accuracy rate is above 94.3%. Since late young red blood cells become mature red blood cells when they grow up, the spectral characteristics of their cytoplasm are very close to those of mature red blood cells, so it is easy to misclassify. Judging from the current situation, the accuracy rate has reached 85.7%.
图像分割的最终目的是为了进行分类识别。为了能够有效的进行识别,必须先建立特征库。建立特征库的方法是,先收集正常人群、各期白血病类型的病例玻片300例,采集它们的显微多光谱图像,并在专家指导下进行人工分类,编制程序进行手动精确分割和测量,进行特征提取。由于提取的参数众多,且多光谱图像有很多个波段,因此,它的光谱特征要比普通的灰度图像多出数倍。而太多的特征不仅会使分析的速度变慢,而且也可能使分析的结果出现更多的误差。因此,进行特征选择和特征筛选,降低特征的维数是很有必要的。对于训练样本,本发明提取了形态学参数、光密度参数和纹理参数以及光谱比值参数共计438个特征,本发明采用遗传算法来进行特征选择,分析不同类型白血病、不同类型细胞的光谱和形态特性,分析它们的特异性和相关性,挑选细胞分类的特征参数,取得了良好的效果。经过遗传算法,最后得到最优特征132个,特征选择完毕后,我们就利用选择后的特征建立原始特征数据库。The ultimate goal of image segmentation is to perform classification and recognition. In order to be able to identify effectively, it is necessary to establish a feature library first. The method of establishing the feature library is to first collect 300 case slides of normal people and various stages of leukemia, collect their microscopic multi-spectral images, and perform manual classification under the guidance of experts, and compile programs for manual and accurate segmentation and measurement. Perform feature extraction. Due to the large number of extracted parameters and the multi-spectral image has many bands, its spectral features are several times more than ordinary grayscale images. Too many features will not only slow down the analysis speed, but also may cause more errors in the analysis results. Therefore, it is necessary to perform feature selection and feature screening to reduce the dimensionality of features. For the training samples, the present invention extracts a total of 438 features including morphological parameters, optical density parameters, texture parameters, and spectral ratio parameters. The present invention uses a genetic algorithm to select features and analyze the spectra and morphological characteristics of different types of leukemia and different types of cells , analyze their specificity and correlation, select the characteristic parameters of cell classification, and achieved good results. After the genetic algorithm, 132 optimal features are finally obtained. After the feature selection is completed, we use the selected features to establish the original feature database.
原始特征库建立好了以后,构建一个3层神经网络,利用神经网络对这些样本进行训练,网络训练完毕后把训练的权值数据存入数据库。After the original feature library is established, build a 3-layer neural network, use the neural network to train these samples, and store the trained weight data in the database after the network training is completed.
有了原始特征库和神经网络训练的结果,我们就可以对未知样本进行识别。本发明采用神经网络对待测细胞进行自动识别分类,取得了良好的结果,而且速度极快。从识别效果看,对于外周血涂片,对Wright染色的约8000个细胞进行分类,单核细胞正确率为97.2%、淋巴细胞正确率为98.7%、中性杆状核粒细胞正确率为95.3%、中性分叶核粒细胞正确率为96.6%、嗜酸性粒细胞正确率为97.6%,嗜碱性粒细胞正确率为92.1%。对于Wright染色的骨髓细胞涂片,对约9000个细胞进行分类,准确率为:常见的晚幼红细胞94.6%、中幼红细胞93.2%、早幼红细胞90.7%、中性分叶核95.5%、中性杆状核87.4%、中性中幼粒细胞89.4%、中性晚幼粒细胞88.3%、淋巴细胞92.0%,对于比较少的嗜酸性和嗜碱性细胞以及其它细胞均在80.3%以上。With the original feature library and the results of neural network training, we can identify unknown samples. The invention adopts the neural network to automatically identify and classify the cells to be tested, and obtains good results, and the speed is extremely fast. From the perspective of recognition effect, for peripheral blood smears, about 8000 cells stained by Wright were classified, the correct rate of monocytes was 97.2%, the correct rate of lymphocytes was 98.7%, and the correct rate of neutrophils was 95.3%. %, the correct rate of neutrophils was 96.6%, the correct rate of eosinophils was 97.6%, and the correct rate of basophils was 92.1%. For the bone marrow smear stained by Wright, about 9000 cells were classified, and the accuracy rate was: 94.6% for common metamyelocytes, 93.2% for mesoerythroblasts, 90.7% for promyelocytes, 95.5% for neutral segmented nuclei, and 95.5% for mesenchymal 87.4% rod-shaped nuclei, 89.4% neutrophil myelocytes, 88.3% neutrophil metamyelocytes, 92.0% lymphocytes, less eosinophils and basophils and other cells are above 80.3%.
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