AT525533A1 - Method and system for analyzing a blood sample - Google Patents

Abstract

According to the invention, a method for analyzing a blood sample is provided, in which, in a first step, an image of a blood sample is recorded by a camera and, in a second step, the image is evaluated by a computer, with the cells visible on the image being classified. in which, based on the classification carried out in the second step, the image is re-evaluated in a third step, with the cells visible on the image being reclassified.

Description

Cells are classified.

The invention further relates to a system for analyzing a blood sample, in particular for carrying out a method according to the invention, comprising a receptacle for the blood sample, a camera and a computer connected to the camera, which is designed to evaluate the images received from the camera, the on the

Cells visible in the image are classified.

When analyzing blood samples, the blood sample from a human or an animal is examined with regard to the quantity and/or the quality of the blood cells it contains. From such an examination, far-reaching conclusions can be drawn about the condition and the microcirculation in the tissue, the existing immune protection and the overall health of the body in general

pull.

Several methods for analyzing a blood sample are known from the prior art in order to determine the cell composition and the cell morphology as part of a blood count

determine.

The easiest way is for a doctor or other trained person to manually analyze a blood sample using a conventional

microscope. The blood sample is placed on a slide

of the user depends.

In order to eliminate these disadvantages, hematology devices can be used to carry out automatic blood analyses. A variety of different systems exist for counting or classifying the blood cells in a blood sample. Most such devices are based on measuring the change in average electrical conductivity between two electrodes placed in a liquid, each placed in a chamber separated by a narrow opening (Coulter counter). The opening is slightly larger than the cells or particles to be measured. The cells or particles to be measured flow from one chamber into the other chamber by means of a flow generated, for example, by negative pressure, so that the electrical resistance between the two electrodes changes. Since the change is proportional to the size of the cell or particle, both the number and the size of the cells or

Particles are detected and evaluated.

blood cells are determined.

Today's analyzers use the flow cytometry method to detect and count white blood cells, in which the cells are passed in front of an electrical voltage or a beam of light. The resulting scattered light is captured and evaluated. In particular, this enables the individual types of white blood cells to be distinguished and counted accordingly. The disadvantage of this method, however, is that immature forms of granulocytes (promyelocytes, myelocytes, metamyelocytes and ligaments) cannot be effectively distinguished from one another and counted. Furthermore, such analyzers do not allow for the detection of nuclear and cytoplasmic changes or pathological granularity in the evaluation of leukocyte lineage. Again, many parameters are calculated and estimated, which

there is a high error rate.

More reliable automated analyzers are based on automated microscopy, in which images of the blood sample are taken through a camera and microscope and

these images subsequently based on corresponding

measurements.

A disadvantage of such known systems is that some pathological cells are visually very similar to one another and can only be distinguished from one another, for example, by their granularity or other inconspicuous details. Even if the algorithms used are designed to also recognize small differences, the optical similarities often lead to incorrect determinations or incorrect classifications and thus to

an incorrect analysis result.

It is therefore an object of the invention to increase the analysis reliability in a method of the type mentioned at the outset and to reduce or avoid misclassifications of cells. In particular, a simple, reliable and fast method for

Analysis of a blood sample are provided.

According to the invention in a method of the type mentioned that, based on the classification carried out in the second step, in a third step, the image is re-evaluated, wherein the

cells shown in the image are reclassified.

It is therefore provided that an image (photo) of a blood sample is first recorded. The image may depict the entire blood sample or, preferably, a portion of the blood sample. This image is evaluated using a first algorithm, each detected cell

in particular by their shape, their size, their colour

thus the result of the blood analysis compared to a

is improved.

This is possible because the second mathematical algorithm is specially tailored to a specific clinical picture recognized in the second step by the first algorithm and is therefore able to calculate even the smallest optical ones

differences between the individual cells.

The classification is carried out, for example, by the image being evaluated by an image acquisition system, with the number of cells, their color, size and shape being determined, for example. These values can, for example, be transferred to numeric scales and stored. Based on these determined values, the first algorithm, which further processes the determined values and, for example, weights them, is then used to determine which types of cells are in the image

were recognized.

During reclassification, the detected cells are examined again, this time using a different, second one

Algorithm, and again assigned to a cell type.

The camera can be any device that can capture an image and store it in the form of (electronic) data on a storage medium so that it can be viewed by a

Algorithm can be evaluated.

The computer is preferably a digital computer and can

e.g. as a workstation computer or tablet computer

be trained.

carry out the third step of the process.

In the classification or reclassification, for example, the red blood cells and the blood platelets are classified according to their size, color and shape and their concentration in the blood is determined. The greater the proportion of morphologically altered and destroyed blood cells, the more pronounced are tissue hypoxia and

microcirculation disorders.

In a preferred embodiment, it is provided that the reclassification in the third method step is carried out only with regard to a specific cell type, with, for example, only the white blood cells being reclassified. In particular, this enables the use of an even more specific algorithm, which is specialized for this type of cell and is appropriately designed or trained, and can therefore deliver an even more precise result.

Alternatively, all cell types are reclassified.

After the third step, the result of the blood analysis, ie in particular the number of individual recognized blood cells, is preferably stored on a data carrier and/or output on an output unit, for example a screen. This enables the data to be stored, for example, for later use or to be displayed to a user in order to draw appropriate conclusions from the data

pull.

a quick and easy update of the algorithms.

The method is (partially) computer-implemented, with the necessary algorithms and other program parts for carrying out the method being stored in particular on a data carrier

are saved.

Before the first method step, the blood sample is optionally prepared accordingly, e.g. applied to a carrier and dried and then, e.g. placed in a recording of a camera-microscope combination, so that images of the blood sample are taken by the camera

can.

White blood cells are classified according to their distinctive size, color, and shape. For example, the white blood cells during

divided into the following groups in the analysis:

®° Basophils (Involved in the development of

allergic reactions)

® Eosinophils (main task is to combat

multicellular parasite)

®° Segmented Neutrophils (Largest group of

granulocytes) ®* ribbon neutrophils (young, immature neutrophils)

®° Lymphocytes (Provide humoral immunity and cellular immunity and regulate the activity of others

cell types)

in the total number of lymphocytes in the blood)

In addition to these normal blood components, pathological cells can also be contained in the blood, e.g

following:

®° Promyelocytes (myeloid cells, the precursors of granulocytes during granulopoiesis, developing from myeloblasts into myelocytes)

° Myelocytes (Young cells of the granulocytic series, usually found in the bone marrow but not in the peripheral ones

blood before)

ee Metamyelocytes (Myeloid cells undergoing granulopoiesis and derived from myelocytes)

®° blasts (young, immature cells produced in the bone marrow)

° Plasma cells (immunocompetent cells that produce antibodies. Normally they make up no more than 0.5% of white blood cells)

ee Atypical lymphocytes (the presence of these cells in the blood indicates an acute infection and thus the need for antibody synthesis)

®° Reactive lymphocytes (cytotoxic lymphocytes that have increased in size due to stimulation by an antigen)

® Prolymphocytes (progenitor cells of lymphopoiesis, the direct precursors of lymphocytes, which arise from lymphoblasts)

Provision is preferably made for a first neural network to be used for classification in the second step. The first mathematical algorithm used in the second step includes or is formed by a first (artificial) neural network. Neural networks are highly efficient when complex patterns need to be recognized and classified during image processing. The first neural network is preferably designed or trained to be able to analyze blood samples of all types, in particular normal, healthy blood samples with at most slight deviations from the norm, such as reactive lymphocytes or a slight left shift, in particular to be able to analyze all possible cells capture and categorize. In order to make this possible, it is preferably provided that the first neural network is trained with a large number of blood counts which are healthy or deviate only slightly from the norm. With this training, the first neural network is better at recognizing and classifying healthy, i.e. at least essentially normal, blood counts that do not contain malignant cells and serious pathological conditions

exhibit.

Furthermore, it is preferably provided that a second neural network is used for the classification in the third step. The mathematical algorithm used in the third step includes or is formed by a second (artificial) neural network, which differs from the first mathematical algorithm or from the first neural network and is specifically intended and designed for a specific blood count classified in the second step . The second neural network

is therefore preferably trained or trained to

to distinguish pathological cells from each other and to be able to categorize them accordingly. In order to make this possible, it is preferably provided that the second neural network is trained (exclusively) with a large number of sick blood counts, ie blood counts that deviate from the norm, in particular malignant cells, rare and/or pathological clinical pictures. With this training, the second neural network is better at recognizing, distinguishing and classifying very immature, malformed and deformed cell groups, as it has a high sensitivity to small deviations

in nuclei and cytoplasm of immature cells.

Thus, the first algorithm is better at detecting immature cells and classifying normal cells, while the second algorithm is better at distinguishing between individual groups of immature cells and classifying them

classify.

Neural network training involves estimating the parameters of the neural network to enable the most accurate classification of blood cells possible. Here, an adjustment of the parameters of the neural network is achieved using training data, ie blood counts that have already been classified, which are supplied to the neural network

lead to more accurate results.

If only the first algorithm is used for classification, only poor accuracy in discriminating between immature groups of cells is achieved, since the first algorithm does not (reliably) understand the fine details in distinguishing these groups of cells.

recognizes. If, on the other hand, only the second algorithm is used

classification of a normal blood sample will result in an overly sensitive analysis susceptible to

false positive detections and is therefore also imprecise.

In a preferred embodiment, it is provided that, based on the classification carried out in the second step, a further image of another partial area of the blood sample is recorded and this further image is subjected to the second and the third step. It is provided here, for example, that a specific predefined number of certain blood parts, for example red blood cells and/or white blood cells, must be recognized and classified accordingly by the first algorithm in order to enable a meaningful analysis. If the corresponding number of blood parts is not present in the first image and accordingly not enough blood parts are classified by the first mathematical algorithm, another image of a different area of the blood sample can be recorded by the camera, e.g. in an area adjacent to the first image the blood sample. Like the first image, this further image is also processed according to the second step and then gg£. examined and evaluated according to the third step. If too few blood parts are recognized and classified again, a further, third image and possibly further images can be recorded. The recording of further images can preferably be made possible by a mechanical displacement device, which is preferably connected to the computer, for example via a control unit, and is optionally controlled accordingly by the computer. The individual images preferably do not overlap one another and particularly preferably border one another directly

to enable the blood sample to be analyzed as accurately as possible.

The displacement device can displace the blood sample relative to the camera and/or the camera relative to the blood sample in order to allow the creation of an image of a recording area of the blood sample that is independent of the first image

make possible.

According to the invention, a system for analyzing a blood sample, in particular for carrying out a method according to the invention, is also provided, comprising a receptacle for the blood sample, a camera and a computer connected to the camera, which is designed to evaluate the images received from the camera, the cells visible on the image are classified, the computer being further designed to re-evaluate the image based on the classification of the cells, the cells visible on the image

be reclassified.

A microscope is preferably also provided here in order to enlarge the image of the blood sample. The magnification of the microscope is preferably adjustable and the microscope is particularly preferably connected to the computer and can optionally be controlled by it, for example

change magnification.

Furthermore, the system preferably includes a displacement device in order to move the receptacle for the blood sample relative to the camera, in particular to translate it. Alternatively or additionally, the displacement device can preferably be designed to move the camera relative to the blood sample, in particular

to move translationally.

The invention is explained in more detail below with reference to an exemplary embodiment shown schematically in the drawing. 1 shows a method according to the invention in a schematic representation, FIG. 2 shows a schematic representation of a system according to the invention, and FIG. 3 shows some examples

Image sections of blood cells.

In Fig. 1 a method according to the invention is shown schematically. At 1 the method is started after a blood sample to be examined has been placed in a photograph of a microscope. At 2, an image of part of the blood sample is now made using a camera (step 1). The image is then fed to a computer and evaluated by it using a first mathematical algorithm, in particular a first neural network, with the cells recognized in the image being classified, for example depending on the color, size and shape of the individual cells. At 3 it is determined whether a first cell type (e.g. red blood cells) has been recognized in a sufficient quantity on the image. If not, the classification of the image is first completed at 4 and the recording area of the camera is then changed at 5 so that another partial area of the blood sample can be captured by the camera, creating another image that is also analyzed and the detected cells classified become. This is continued until it is determined at 3 that sufficient cells of one type (e.g. red blood cells) have now been recorded and classified on the images recorded up to now. It is then checked at 6 whether a second

Cell type (e.g. white blood cells) in a sufficient

quantity has been detected on the images taken so far. If not, the detected cells are analyzed at 7 and the field of view of the camera is changed at 5 in order to obtain an image of another partial area of the blood sample, which is also examined again. If sufficient cells of the second cell type have been identified, the classified cells are classified according to several criteria

8, 9, 10, 11 and 12, for example, checked as follows:

At 8: number of blasts and promyelocytes greater than 9

If 9: Number of lymphocytes, reactive lymphocytes, atypical lymphocytes, large granular lymphocytes and plasma cells is greater than 0 and the ratio of reactive lymphocytes, atypical lymphocytes, large granular lymphocytes and plasma cells to lymphocytes, reactive lymphocytes, atypical lymphocytes, large granular lymphocytes and plasma cells is greater than 0 greater than 0.2

At 10: Banded neutrophils to leukocytes ratio is greater than 0.1

At 11: ratio of myezolytes and metamylocytes (IMGRA) to leukocytes is greater than 0.04

At 12: Banded Neutrophils to White Cells Ratio is greater than 0.07 and Myezolytes to Metamylocytes (IMGRA) to White Cells Ratio is greater than 0.02.

If none of the conditions 8,9,10,11 or 12 are met, the blood sample is classified as "normal" at 13, the results are stored at 14 and the procedure

completed at 15.

If one of the conditions 8,9,10,11 or 12 is met, the blood sample at 16 is considered as

classified as "pathological". Based on which of the conditions is met, a second mathematical algorithm is then selected, e.g. from a database, and the recognized cells are analyzed and classified at 17 again. This ensures that the cells are recognized and assigned in the best possible way according to the condition of the blood sample, so that incorrect classifications are avoided or reduced as far as possible. After reclassification at 17, the result becomes 14

saved and the method ends at 15.

The cells are classified using the classification or the

Reclassification e.g. divided as follows:

White Red Blood Cells Platelets

blood cells

Basophil Size Color Shape Microplatelets

Eosinophilic microcytes | polychromy poikilo macro

cytes platelets Promyelo macrocytes | Hypochromia Codo Normal cytes cytes Platelets Myelocytes Normal hyperchromia | Schisto size cytes Band normochromia | Helm neutrophils Segmented sickle neutrophils Lymphocytes Spherocytes

Monocytes Ellipto cells Plasma cells Ovalo cells Reactive tears Lymphocytes Droplet cells Large stomato granular cells Lymphocytes Prolympho acanthocytes cells Atypical echino lymphocytes cells Blasts Normal shape

FIG. 2 shows a system according to the invention, which is designed to carry out a method according to FIG. The system includes a microscope 18, a camera 19, a receptacle 20 for a blood sample, a computer 21, an output unit 22 designed as a screen, a control unit 23 and a displacement device 24. The computer 21 controls the method and receives the data from the camera 19 Images of the blood samples to subsequently evaluate them and classify the detected cells and, if necessary, select the appropriate algorithms and reclassify the cells. The displacement device 24 is designed to move the recording 20 relative to the camera 19 or the camera 19 relative to the recording 20 in all spatial directions as required, on the one hand

Generation of images of several partial areas of the blood sample

to allow and on the other hand to improve the focusing of the camera 19 on the blood sample. The result of the blood analysis, in particular the classification, can be found on the

Output unit 22 are shown.

In FIG. 3, some image sections of blood cells are shown as examples, each of which has great visual similarity to one another. Lymphocytes 25, prolymphocytes 26 and atypical lymphocytes 27 are shown in FIG. 3a. In Fig. 3b monocytes 28, blasts 29 and reactive lymphocytes 30 are shown. In Fig. 3c, promyelocytes 31, myelocytes 32 and metamyelocytes 33 are shown.

Claims (7)

Patent Claims: 1. A method for analyzing a blood sample, in which in a first step an image of a blood sample is recorded by a camera, and in a second step the image is evaluated by a computer, the cells visible on the image being classified, characterized in that that, based on the classification carried out in the second step, in a third step the image is re-evaluated, wherein the on the image apparent cells are reclassified. 2. The method according to claim 1, characterized in that in the second step, a first neural network for classification is used. 3. The method according to claim 1 or 2, characterized in that in the third step a second neural network is used for classification. 4. The method according to any one of claims 1 to 3, characterized in that, based on the classification carried out in the second step, another image of another area of the blood sample is recorded and this image is subjected to the second and third steps. 5. System for analyzing a blood sample, in particular for carrying out a method according to one of claims 1 to 4, comprising a receptacle for the blood sample, a camera and a computer connected to the camera, which is designed to evaluate the images received from the camera , where the cells shown in the picture be classified, characterized in that the Computer is further designed to re-evaluate the image based on the classification of the cells, wherein the cells visible on the image are reclassified become. 6. System according to claim 5, characterized in that a microscope is further provided to the image of enlarge blood sample. 7. System according to claim 5 or 6, characterized in that a displacement device is provided in order to move the receptacle for the blood sample relative to the camera, in particular to move translationally.