JP3958340B2 - Visualization analysis method for positive cells in stained tissue specimens - Google Patents
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Description
本発明は、組織学、病理学の分野における染色された組織標本における陽性細胞を客観的に定量するための方法に関する。 The present invention relates to a method for objectively quantifying positive cells in stained tissue specimens in the fields of histology and pathology.
組織学、病理学の分野において、組織標本を免疫学的手段やハイブリダイゼーション等により染色し、その陽性細胞を定量解析することは、これらの分野における研究手段として、また病理学的診断の手段として極めて重要であり、広く実施されている。陽性細胞数の定量については、画像から実験者自ら設定した領域について、計数するという方法が用いられている（非特許文献１、２）。
しかしながら、従来の定量方法では、実験者自らが設定した領域のみを定量するので、実験者が注目していない場所のデータを得ることができない、得られるデータは計数データのみであり設定した領域内の空間の情報が失われてしまうという問題があった。
従って、本発明の目的は、染色された組織標本における陽性細胞数を定量解析する新たな方法を提供することにある。
However, in the conventional quantification method, only the area set by the experimenter himself is quantified, so it is not possible to obtain the data of the place where the experimenter is not paying attention. The obtained data is only the count data and is within the set area. There was a problem that the information of the space was lost.
Accordingly, an object of the present invention is to provide a new method for quantitatively analyzing the number of positive cells in a stained tissue specimen.
そこで本発明者は、組織標本を撮像した画像のコンピュータによる処理手段について種々検討したところ、画像処理工程において染色細胞からノイズを排除するため染色領域の検出に加えて細胞の大きさによる検出を組み合せ、さらに陽性細胞像を擬似カラー化して陽性細胞密度として検出し、さらにその着色像を標準化するとともに、複数の標本を用いて平均値によるマップを作成することにより、領域設定が客観的になるとともに標準化と平均値マップの作成により空間情報を維持した上での定量データが可視化できることを見出した。 Therefore, the present inventor has examined various processing means using a computer for images of tissue specimens. In order to eliminate noise from the stained cells in the image processing step, the detection based on the cell size is combined with the detection of the stained region. In addition, the positive cell image is pseudo-colored and detected as the positive cell density, and the colored image is standardized, and by creating a map with an average value using multiple specimens, the area setting becomes objective We found that quantitative data can be visualized while maintaining spatial information by standardization and creation of an average value map.
すなわち、本発明は、染色された組織標本を撮像し、得られた画像をコンピュータにより処理して染色陽性細胞を可視化して解析する方法であって、（１）一定の閾値以上に染色された領域を検出する工程、（２）検出された領域のうち、一定の大きさの細胞が染色された部分のみを陽性細胞像として選択する工程、（３）画像を碁盤目状のピクセルに区切り、各ピクセル内の陽性細胞密度を測定する工程、（４）陽性細胞密度に応じた擬似カラーを付し、組織標本全体の着色像を得る工程、（５）組織標本全体の形状を既知の組織標本形状に合わせて標準化を行う工程、及び（６）複数の個体由来の組織標本について前記（１）〜（５）の操作を行い、複数の組織標本についての着色像の平均値マップを得る工程を含むことを特徴とする染色された組織標本の陽性細胞の可視化解析方法を提供するものである。 That is, the present invention is a method of imaging a stained tissue specimen and visualizing and analyzing staining positive cells by processing the obtained image with a computer, and (1) stained above a certain threshold A step of detecting a region, (2) a step of selecting only a portion where cells of a certain size are stained as a positive cell image from among the detected regions, (3) dividing the image into grid-like pixels, A step of measuring the positive cell density in each pixel, (4) a step of obtaining a colored image of the whole tissue specimen by applying a pseudo color corresponding to the positive cell density, and (5) a tissue specimen whose shape of the whole tissue specimen is known. A step of standardizing in accordance with the shape, and (6) a step of performing the operations (1) to (5) for tissue specimens derived from a plurality of individuals to obtain an average map of colored images for the plurality of tissue specimens. Dye characterized by containing Has been visualized analysis method of positive cells of the tissue specimen is to provide.
本発明によれば、標本の領域設定及び定量がコンピュータにより自動的にでき、かつ複数の標本のデータが同時に解析できるため一点だけのデータでなく空間情報を加味したデータの可視化が可能となった。さらに、本発明によれば、グループ間の統計比較も容易にできる。さらに、薬物投与や行動訓練といった実験操作を行った実験動物の組織標本について、実験者の意図しない領域の組織変化を明瞭にかつ客観的に可視化することが可能となった。病理診断の現場においてもこの方法を適用することで、客観的な診断が可能になる。また、本発明は、細胞以外の粒子状像等の定量にも適用可能である。 According to the present invention, sample area setting and quantification can be automatically performed by a computer, and data of a plurality of samples can be analyzed simultaneously, so that not only single-point data but also data including spatial information can be visualized. . Furthermore, according to the present invention, statistical comparison between groups can be facilitated. In addition, it became possible to clearly and objectively visualize tissue changes in regions not intended by the experimenter for tissue specimens of experimental animals subjected to experimental operations such as drug administration and behavior training. By applying this method also at the pathological diagnosis site, an objective diagnosis becomes possible. The present invention is also applicable to quantification of particulate images other than cells.
本発明においては、まず、染色された組織標本を撮像し、得られた画像をコンピュータに入力する。ここで、組織標本としては、ヒトを含む動物、植物等の生体組織から採取した組織標本が用いられる。例えば、臓器全体像の凍結切片、手術により摘出した組織の切片等が挙げられる。また、染色手段としては、細胞核や細胞体のみが濃染する対象に対する免疫染色、ｉｎ ｓｉｔｕ ｈｙｂｒｉｄｉｚａｔｉｏｎあるいは、核染色等が挙げられる。撮像には、顕微鏡と撮像装置を用いるのが好ましい。撮像装置としては、例えばカラーＣＣＤカメラ等のディジタル画像撮影が行えるカメラが用いられる。撮像装置により得られた画像は、ディジタル信号の画像データに変換されたコンピュータに送られる。 In the present invention, first, a stained tissue specimen is imaged, and the obtained image is input to a computer. Here, as the tissue specimen, a tissue specimen collected from biological tissues such as animals including humans and plants is used. For example, a frozen section of a whole organ image, a section of tissue removed by surgery, and the like can be mentioned. Examples of staining means include immunostaining, in situ hybridization, nuclear staining, and the like for a target in which only cell nuclei and cell bodies are stained. For imaging, a microscope and an imaging device are preferably used. As the imaging device, for example, a camera capable of taking a digital image such as a color CCD camera is used. An image obtained by the imaging device is sent to a computer converted into image data of a digital signal.
コンピュータによる画像処理は、入力装置、表示装置及びコンピュータにより行われる。 Image processing by a computer is performed by an input device, a display device, and a computer.
入力装置は、本発明方法の実施に関する指示入力の受付、各種文字及び記号を含むデータの入力等を行うための装置である。具体的には、前述した指示、データ等の入力に用いることができる、キーボード、マウス、タッチパネル、音声入力機器等の機器の組合せにより構成される。 The input device is a device for receiving an instruction input related to the implementation of the method of the present invention, inputting data including various characters and symbols, and the like. Specifically, it is configured by a combination of devices such as a keyboard, a mouse, a touch panel, and a voice input device that can be used for inputting the above-described instructions and data.
表示装置は、メニュー画面、操作画面、指示画面等の他、取得した画像、計測結果、着色像等の表示を行うためのものである。具体的には、液晶、プラズマ等のフラットパネルディスプレイ、ＣＲＴ等の表示管により画像の表示が行える装置が用いられる。この他に、拡大投影表示するための、スライドプロジェクタ等を接続することもできる。 The display device is for displaying an acquired image, a measurement result, a colored image, and the like in addition to a menu screen, an operation screen, an instruction screen, and the like. Specifically, a device capable of displaying an image using a flat panel display such as liquid crystal or plasma, or a display tube such as a CRT is used. In addition, a slide projector or the like for displaying an enlarged projection can be connected.
コンピュータは、中央処理ユニット（ＣＰＵ）と、メモリと、補助記憶装置とを有する。補助記憶装置には、ＣＰＵが実行するプログラム群、各種データ等が格納される。 The computer has a central processing unit (CPU), a memory, and an auxiliary storage device. The auxiliary storage device stores a program group executed by the CPU, various data, and the like.
以下の画像処理は、すべてコンピュータ上で行われる。 The following image processing is all performed on a computer.
まず、染色された組織標本を撮像して得られた画像（図１）は、公知の画像処理ソフトウェア（ＮＩＨ−ｉｍａｇｅ）等で、バックグラウンドのノイズを取り除くことが好ましい（図２）。この画像から、（１）一定の閾値以上に染色された領域を検出する。この操作は、図３のように擬似カラー化して行うのが望ましい。ここで、一定の閾値は、実験者の経験から通常判断される閾値でもよい。この操作は、例えばＭＡＴＬＡＢソフトウェア上で行列データとして表現された画像から一定の数値以上のデータを選び出してくる操作を行うことで実現可能である。閾値については陽性像の存在しないバックグラウンドの値を基準にその２倍程度の濃度の領域を陽性像と捉えることが適していると考えられる。 First, it is preferable to remove background noise from an image obtained by imaging a stained tissue specimen (FIG. 1) with known image processing software (NIH-image) or the like (FIG. 2). From this image, (1) a region stained above a certain threshold is detected. It is desirable to perform this operation with pseudo color as shown in FIG. Here, the fixed threshold value may be a threshold value that is normally determined from the experience of the experimenter. This operation can be realized, for example, by performing an operation of selecting data of a certain numerical value or more from an image expressed as matrix data on the MATLAB software. With regard to the threshold, it is considered appropriate to regard a region having a density about twice that of a background value where no positive image exists as a positive image.
この際、標本中のゴミや切片の傷などのノイズのデータへの混入を防ぐために、陽性細胞としてふさわしい（２）サイズの一定の大きさの細胞が染色された部分のみを陽性細胞として選択する。この部分のみを陽性像とする。ここで、一定の大きさの細胞の選択は、細胞サイズの大きさ、又は細胞核の大きさで判定することができる。この操作は、例えばＭＡＴＬＡＢソフトウェアに付加することができるｉｍａｇｅ ｐｒｏｃｅｓｓｉｎｇ ｔｏｏｌｂｏｘに含まれる選択領域の面積を計算するライブラリ関数を利用することで実現できる。このようにして計算した各領域の面積のうち一定の範囲内のもののみを陽性細胞像として選び出すことが可能である。 In this case, in order to prevent contamination of the sample with noise such as dust in the specimen and scratches on the section, only a portion where cells of a certain size suitable for positive cells are stained is selected as positive cells. . Only this part is a positive image. Here, selection of cells having a certain size can be determined by the size of the cell size or the size of the cell nucleus. This operation can be realized, for example, by using a library function that calculates the area of the selected region included in the image processing toolbox that can be added to the MATLAB software. Only areas within a certain range among the areas of the respective areas calculated in this way can be selected as positive cell images.
次に、（３）画像を碁盤目状のピクセルに区切り、各ピクセル内の陽性細胞密度を測定する（図４）。この陽性細胞密度の計数は、計数ソフトウェアにより自動的に行われる。各ピクセルの大きさは、例えば示した例の場合、２００μｍブロックによって９等分とすることができる。この操作は、例えば、ＭＡＴＬＡＢソフトウェアに付加することができるｉｍａｇｅ ｐｒｏｃｅｓｓｉｎｇ ｔｏｏｌｂｏｘに含まれるライブラリ関数群によって実現できる。選択領域の重心を計算する関数によって各領域を１点で表すように変換し、この変換データに対してブロックごとに任意の数値演算を行うことが可能な関数によってブロック内の平均値を求めることで陽性細胞密度を計算することができる。 Next, (3) the image is divided into grid-like pixels, and the positive cell density in each pixel is measured (FIG. 4). This counting of positive cell density is performed automatically by counting software. For example, in the case of the illustrated example, the size of each pixel can be divided into nine equal parts by a 200 μm block. This operation can be realized by, for example, a library function group included in an image processing toolbox that can be added to the MATLAB software. Convert each area to be represented by one point using a function that calculates the center of gravity of the selected area, and obtain an average value in the block using a function that can perform arbitrary numerical operations on this converted data for each block. To calculate the positive cell density.
（４）各ピクセルを、陽性細胞密度に応じた擬似カラーを付し（図５）、組織標本全体の着色像を得る（図６）。これにより、組織標本全体で陽性細胞密度の高い領域がスクリーニングでき、それを着色で可視化した画像が得られる。この操作は、例えば、ＭＡＴＬＡＢソフトウェアに含まれる画像データを任意のカラーマップによって表示する機能によって実現できる。 (4) Each pixel is given a pseudo color corresponding to the positive cell density (FIG. 5) to obtain a colored image of the entire tissue specimen (FIG. 6). Thereby, the area | region with a high positive cell density can be screened in the whole tissue specimen, and the image which visualized it by coloring is obtained. This operation can be realized by, for example, a function of displaying image data included in the MATLAB software with an arbitrary color map.
しかし、前記工程（４）で得られた画像は１個体１切片の結果である。全体の傾向をするには複数の切片、複数の個体から得られたデータを平均化する必要がある。しかし組織標本は、個体、条件によって形状が微妙に異なるので、そのままの形では複数の標本の画像と重ねあわせることができない。そこで、（５）組織標本全体の形状を既知の組織標本形状に合わせて標準化を行う工程が必要となる。この標準化は、既知の組織標本形状のデータ、例えば既知の脳地図のデータ（非特許文献２）をもとに、前記（４）の着色像を回転、拡大、縮小等を行って、データの標準化を行う（図７）。この標準化により、（４）で得られた着色画像を他の標本の画像と重ねあわせて平均値マップを作成可能なもととなる。この操作は、例えばＭＡＴＬＡＢソフトウェアに付加することができるｉｍａｇｅ ｐｒｏｃｅｓｓｉｎｇ ｔｏｏｌｂｏｘに含まれるライブラリ関数群によって実現できる。任意の領域を選択する関数により切片全体像を選び出し、これを楕円に近似して特徴抽出する関数により長軸と短軸、回転角度を算出する。この値に基づき画像操作を行う関数により変換することで標準化データを得ることができる。ＭＡＴＬＡＢソフトウェア上でこの情報は行列データとして表現されており、行列演算という形で各々のマップから平均値マップを計算することが可能である。 However, the image obtained in the step (4) is the result of one slice per individual. To obtain the overall trend, it is necessary to average data obtained from a plurality of sections and a plurality of individuals. However, since the shape of the tissue specimen differs slightly depending on the individual and the conditions, it cannot be superimposed on the images of a plurality of specimens as they are. Therefore, (5) a step of standardizing the shape of the entire tissue specimen according to the known tissue specimen shape is required. This standardization is based on known tissue specimen shape data, for example, known brain map data (Non-Patent Document 2), by rotating, enlarging, reducing, etc. the colored image of (4) above. Standardization is performed (FIG. 7). By this standardization, it becomes possible to create an average value map by superimposing the colored image obtained in (4) with images of other specimens. This operation can be realized by, for example, a library function group included in an image processing toolbox that can be added to the MATLAB software. An entire section image is selected by a function for selecting an arbitrary region, and a major axis, a short axis, and a rotation angle are calculated by a function for extracting a feature by approximating the slice. Standardized data can be obtained by conversion using a function that performs image manipulation based on this value. This information is expressed as matrix data on the MATLAB software, and an average value map can be calculated from each map in the form of matrix operation.
（６）複数の個体由来の組織標本について、前記（１）〜（５）の操作を行い、得られた複数の組織標本についての着色像の平均値マップを得る。これは、複数の個体由来の組織標本についての着色像を重ねあわせて、各ポイントごとの平均値を求め、それをマップ化すればよい（図８及び９）。 (6) For the tissue specimens derived from a plurality of individuals, the operations (1) to (5) are performed, and an average value map of colored images for the obtained plurality of tissue specimens is obtained. This can be done by superimposing colored images of tissue specimens derived from a plurality of individuals, obtaining an average value for each point, and mapping it (FIGS. 8 and 9).
さらに、（７）複数の条件の個体群について、前記（１）〜（６）の操作を行い、群間の比較を行えば、群間の比較が可能である。図８と図９は、異なる実験操作を行った群についての着色像である。図８と図９では、陽性細胞密度が大きく異なる領域が腹側に存在することが判る。また、陽性細胞密度は、擬似カラー化されているが、定量化されており、定量解析もできる。ここで、組織標本の数は、統計学的有意差検定できる数、例えば５以上が好ましい。 Further, (7) comparison between groups is possible by performing the operations (1) to (6) and comparing the groups of individuals with a plurality of conditions. FIG. 8 and FIG. 9 are colored images for groups in which different experimental operations were performed. 8 and 9, it can be seen that there is a region on the ventral side where the positive cell density is greatly different. The positive cell density is pseudo-colored, but is quantified and can be quantitatively analyzed. Here, the number of tissue samples is preferably a number that can be statistically significant, for example, 5 or more.
さらに、群間比較の結果を、統計的パラメータとして数値化し、各ピクセルに擬似カラーを付せば、有意差検定で有意差があった部分のみを可視化することもできる。例えば、図９のデータをもとに有意差がある部分（ｔ検定）を擬似カラー化した図が図１０である。図１０によれば、右下の部分のみが、有意に陽性細胞が存在する部分であることがわかる。この操作は、例えばＭＡＴＬＡＢソフトウェアに含まれるｔ検定を行うための関数により実現できる。計算した統計量を対数値に変換する関数を利用し、前記工程（４）に記した方法で計算結果を可視的に表示することが可能である。 Furthermore, if the result of comparison between groups is digitized as a statistical parameter and a pseudo color is assigned to each pixel, only a portion having a significant difference in the significance test can be visualized. For example, FIG. 10 is a diagram in which a portion having a significant difference (t-test) is pseudo-colored based on the data of FIG. According to FIG. 10, it can be seen that only the lower right portion is a portion in which positive cells are significantly present. This operation can be realized by, for example, a function for performing a t-test included in the MATLAB software. Using a function for converting the calculated statistic into a logarithmic value, it is possible to display the calculation result visually by the method described in the step (4).
次に実施例を挙げて本発明をさらに詳細に説明する。 EXAMPLES Next, an Example is given and this invention is demonstrated still in detail.
実施例１
画像解析をプログラミング上で表現する手段としてはＭＡＴＬＡＢソフトウェアを用いた。
１）マイクログリア特異的な標識蛋白ｉｂａ−１に対する免疫染色を行った組織標本（ラット脳）に対して本発明を適用した。その結果、薬物を実験的に投与した群で見られる特定の組織の炎症によって生じたマイクログリア細胞の増加を明らかに検出することができた。
２）電気的な活動に応じて発現が増える標識蛋白ｃ−Ｆｏｓに対する免疫染色を行った組織標本（マウス脳）に対して、課題実行後にｃ−Ｆｏｓ発現細胞核が増える特定の領域を明らかに検出することができた。
図１１は、ｃ−Ｆｏｓ陽性細胞密度について擬似カラー化した画像を平均値マップ化した（工程（５））画像である。図１２は、工程（７）を行い、Ｐ＜０．０５の部分を擬似カラー表示した図である。
Example 1
MATLAB was used as a means for expressing image analysis in programming.
1) The present invention was applied to a tissue specimen (rat brain) that had been immunostained for a microglia-specific labeled protein iba-1. As a result, it was possible to clearly detect the increase in microglial cells caused by inflammation of specific tissues seen in the experimentally administered group.
2) From the tissue specimen (mouse brain) immunostained for the labeled protein c-Fos whose expression increases with electrical activity, a specific region where c-Fos-expressing cell nuclei increase is clearly detected after the task is executed We were able to.
FIG. 11 is an image in which an image obtained by pseudo-coloring the c-Fos positive cell density is converted into an average value map (step (5)). FIG. 12 is a diagram in which the step (7) is performed and the portion of P <0.05 is displayed in pseudo color.
Claims (3)
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