KR102628182B1 - Method and apparatus for multicolor unmixing by mutual information minimization - Google Patents

Abstract

The present disclosure provides a method and device for multicolor separation through minimizing the amount of mutual information. In the present disclosure, a plurality of images are acquired for a plurality, for example, of n fluorescent substances, each labeled with a different biomolecule, and for each of the pairs each consisting of two of the acquired images, each of the pairs Images for each fluorescent material can be separated from the acquired images while reducing the amount of mutual information shared between the images. According to the present disclosure, signals of three or more fluorescent substances whose respective emission spectra overlap in one wavelength band can be separated. Specifically, without measuring the emission spectra of each fluorescent material, images for each fluorescent material can be separated using only images obtained in the same number of detection wavelength bands as the number of fluorescent materials.

Description

METHOD AND APPARATUS FOR MULTICOLOR UNMIXING BY MUTUAL INFORMATION MINIMIZATION}

The present disclosure relates to a method and device for multicolor separation through minimizing the amount of mutual information.

Recently, immunotherapy drugs that produce anti-cancer effects by activating immune cells present in the patient's cancer tissue have been receiving a lot of attention. Immunoanticancer drugs have large differences in their anticancer effects depending on which immune cells are present in the patient's cancer tissue. In order to select the optimal anticancer agent for each patient or develop an immunotherapy agent with a new mechanism, there is a need to simultaneously image multiple immune biomarkers within the patient's cancer tissue. Many existing multi-marker simultaneous imaging technologies have several disadvantages, such as requiring expensive special equipment, complicated processes and slow imaging speeds, and sample destruction during the imaging process. It is not widely used to predict immunotherapy reactivity. Therefore, in order to recommend optimal immuno-anticancer drugs for each patient and develop new immuno-anticancer drugs, low-cost, high-efficiency, and intact multi-marker simultaneous imaging technology is needed.

The problem to be solved is to provide a multicolor separation method and device by minimizing the amount of mutual information.

A method of operating an electronic device according to the present disclosure includes acquiring a plurality of images for a plurality of fluorescent substances each labeling different biomolecules; And for each of the pairs each consisting of two of the acquired images, images for each of the fluorescent substances from the acquired images while reducing the amount of mutual information shared between each image of the pairs. A separation step may be included.

An electronic device according to the present disclosure includes a memory, and a processor connected to the memory and configured to execute at least one instruction stored in the memory, wherein the processor includes a memory and connected to the memory, and configured to execute at least one instruction stored in the memory. A processor configured to execute at least one stored instruction, wherein the processor acquires a plurality of images for a plurality of fluorescent substances each labeling a different biomolecule, and selects two of the acquired images. For each of the configured pairs, it may be configured to separate images for each of the fluorescent substances from the acquired images, while reducing the amount of mutual information shared between the images of each of the pairs.

A non-transitory computer-readable storage medium according to the present disclosure includes acquiring a plurality of images for a plurality of fluorescent substances each labeled with a different biomolecule, and each of the acquired images with two of the acquired images. For each of the pairs being constructed, performing a method comprising separating images for each of the fluorescent substances from the acquired images while reducing the amount of mutual information shared between the respective images of the pairs. You can save one or more programs for

According to the present disclosure, image separation performance can be improved. At this time, in the present disclosure, signals of three or more fluorescent materials whose emission spectra overlap in one wavelength band can be separated. Specifically, in the present disclosure, images for each fluorescent material can be separated only from images obtained in the same number of detection wavelength bands as the number of fluorescent materials, without measuring the emission spectra of each fluorescent material.

1 is a block diagram of an electronic device according to various embodiments of the present disclosure.
FIG. 2 is a schematic diagram illustrating the operating principle of an electronic device according to various embodiments of the present disclosure.
FIG. 3 is an exemplary diagram for explaining an example operation of an electronic device according to various embodiments of the present disclosure.
Figure 4 is a flowchart of a multi-color separation method through minimizing the amount of mutual information of an electronic device according to various embodiments of the present disclosure.
FIG. 5 is a flow chart specifically illustrating the steps of separating images for each fluorescent material from the acquired images of FIG. 4.

Below, with reference to the attached drawings, embodiments of the present disclosure will be described in detail so that those skilled in the art can easily practice them. However, the present disclosure may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present disclosure in the drawings, parts that are not related to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

Throughout the specification, when a part is said to “include” a certain element, this means that it may further include other elements rather than excluding other elements, unless specifically stated to the contrary. In addition, terms such as "unit", "unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which is implemented as hardware, software, or a combination of hardware and software. It can be.

Although the operating entity may be omitted in the description, the method described in the present disclosure may be implemented in an electronic device, such as a device including a computing device and a fluorescence microscope.

Fluorescence imaging labels biomolecules inside a sample with various fluorophores, excites them with light, and then detects the light emitted from each fluorophore using an optical microscope to identify biomolecules inside the sample. It is a technique that can be observed indirectly. Fluorescent substances have different excitation and emission spectra due to their unique chemical structures, and always emit light with a longer wavelength than the light they absorb. The excitation and emission spectra of fluorescent materials generally have a wide width of about 100 nm within the visible light range (400 - 700 nm). In order to observe multiple biomolecules simultaneously in one sample, it is necessary to label multiple biomolecules with different fluorescent substances and then selectively obtain images of each fluorescent substance. To achieve this, it is necessary to use fluorescent materials whose excitation and emission spectra do not overlap. When four or more fluorescent materials are used simultaneously, the emission spectra overlap between the fluorescent materials due to the wide width of the emission spectrum and they become indistinguishable from each other. Therefore, in general, up to four biomolecules can be detected by exciting only one fluorescent substance per standard excitation wavelength (405, 488, 560, 633 nm).

Recently, the need to simultaneously image more biomolecules in a single sample has increased in the fields of medical diagnosis and research. However, existing fluorescence microscopy techniques have a limitation in that only up to four fluorescent substances can be used simultaneously at a time. To overcome these limitations, various technologies have been developed, and these technologies can be broadly divided into three categories.

First, multi-round staining involves imaging biomolecules inside a sample by labeling them with three or four fluorescent substances whose emission spectra do not overlap, and then deactivating the fluorescent substances through chemical treatment. Alternatively, fluorescent substances are removed from biomolecules. Afterwards, other biomolecules are labeled with the same three or four fluorescent substances and imaged. By repeating this method, it is possible to observe dozens of biomolecules simultaneously in one biological sample. However, it takes a long time because the fluorescent labeling and deactivation process must be repeated, and there is the inconvenience of having to register each image obtained during the repeated process. Additionally, due to matching problems, the distribution of multimolecules cannot be obtained in three dimensions. In addition, there is a problem of sample damage during the chemical treatment process.

Second, in the spectral imaging and signal unmixing technique, several biomolecules are individually labeled with several fluorescent substances with overlapping emission spectra, and then several fluorescent substances are excited simultaneously. Afterwards, images of the sample are obtained in several detection wavelength bands, and the obtained images are separated into images of each fluorescent material only, based on information about the relative intensity of each fluorescent material in each wavelength band. If the emission spectrum of each fluorescent substance labeling various biomolecules is known, it is possible to separate (unmix) the images of each fluorescent substance based on the emission intensity for each wavelength of the fluorescent substance. However, to accurately calibrate the emission spectrum of a fluorescent material, expensive special equipment called a spectral detector is required. In addition, the intensity of each fluorescent material at each wavelength varies depending on the optical characteristics inside the microscope, the sensitivity of the camera at each wavelength, and the chemical composition of the sample. Accordingly, there is the inconvenience of having to measure the emission spectrum of each fluorescent substance separately for each microscope and each sample, making it very difficult to actually use it for tissue imaging.

Third, blind signal separation technique (Blind Unmixing) is a method of separating fluorescent material signals without knowing the emission spectrum of the fluorescent material. For this, Independent Component Analysis (ICA) or matrix analysis without negative values ( Non-negative matrix factorization (NMF) has been used. However, because millions of elements must be accurately inferred at the same time, the accuracy of signal separation is greatly reduced, and its use has been very limited. For example, to separate the signals of three fluorescent substances with overlapping emission spectra, more than 3,145,728 (=3 x 1024 x 1024) elements must be inferred simultaneously (based on 1024 x 1024 resolution). Additionally, existing ICA and NMF have a condition that the number of images (number of rows of the IMG matrix) required for separation must be greater than the number of fluorescent substances (number of rows of the F matrix). In other words, in order to simultaneously image eight fluorescent substances, more than nine images must be obtained, which requires expensive special equipment called a spectral detector.

In conclusion, existing multi-molecule simultaneous imaging techniques are difficult to practically utilize for research and diagnosis due to problems such as the inconvenience and complexity of the experimental and imaging process, signal separation inaccuracy, and the requirement for special equipment, so they are not actively used. To solve this problem, a new blind signal separation-based multimolecular simultaneous imaging technique (Process of ultra-multiplexed Imaging of biomoleCules viA the unmixing of the Signals of Spectrally Overlapping fluorophores; PICASSO) v1.0 and v2.0 was developed. . PICASSO v1.0 and v2.0 are technologies that separate images containing mixed signals of two fluorescent substances into images containing only the signals of each fluorescent substance, using mutual information minimization techniques.

In PICASSO v1.0, the first detection wavelength band is set to include only the signal of the first fluorescent material, and the second detection wavelength band is set to include the signals of both fluorescent materials to obtain two images, IMG1 and IMG2. Since IMG1 contains only the signal of the first fluorescent substance, and IMG2 contains the signals of both fluorescent substances, by subtracting IMG1 from IMG2, an image containing only the signal of the second fluorescent substance can be obtained. At this time, it is important to determine how much IMG1 is to be subtracted. In PICASSO v1.0, α was found that minimizes the amount of mutual information between IMG2 - α x IMG1 and IMG1. This method is based on the assumption that when the signal of IMG1 is completely removed from IMG2 and only the signal of the second fluorescent substance is included, the amount of mutual information between this image and IMG1, which contains only the signal of the first fluorescent substance, is minimized.

PICASSO v2.0 (also known as iterative PICASSO) is an improved version of PICASSOR v1.0. Even when the signals of both fluorescent substances are included in the first detection wavelength, the two images IMG1 and IMG2 are converted to only the signals of each fluorescent substance. This is a technology that can separate the two images it contains. In PICASSO v2.0, the first detection wavelength band can be used more broadly, which has the advantage of increasing the signal-to-noise ratio of IMG1. Additionally, it has the advantage of working well even when there are almost no markers labeled with the first fluorescent substance.

PICASSO v1.0 and v2.0 have several advantages compared to existing signal separation techniques (spectral unmixing). First, since they only require the same number of images as the number of fluorescent substances to be separated, special devices such as spectral detectors are used. It has the advantage of being able to be implemented without equipment and using a filter method. Additionally, while existing signal separation techniques required a separate process to measure the emission spectrum of the fluorescent material being separated, PICASSO v1.0 and v2.0 have the advantage of not requiring such separate measurement. In the case of many fluorescent substances, the emission spectrum varies depending on the chemical environment inside the sample, so it is very difficult to accurately measure the emission spectrum of the fluorescent substance. PICASSO v1.0 and 2.0 do not require this measurement process, so they have the advantage of working well with a variety of samples. When PICASSO v1.0 and v2.0 technologies are applied to five commonly used wavelength bands (UV, blue, green, red, IR), two fluorescent materials can be used for each wavelength band, for a total of 10 fluorescent materials. can be used simultaneously. Additionally, if large Stokes shift fluorescent materials are added for each wavelength band, a total of 15 fluorescent materials can be used simultaneously.

Below, this disclosure proposes PICASSO v3.0, which is improved from PICASSO v1.0 and v2.0. PICASSOR v3.0 is a technology that can separate the signals of three or more fluorescent substances with overlapping emission spectra using only images obtained in the same number of detection wavelength bands as the number of fluorescent substances without measuring the emission spectra of the fluorescent substances separately.

1 is a block diagram of an electronic device 100 according to various embodiments of the present disclosure. FIG. 2 is a schematic diagram illustrating the operating principle of the electronic device 100 according to various embodiments of the present disclosure.

Referring to FIG. 1, the electronic device 100 according to various embodiments of the present disclosure includes at least one of a detector 110, an input module 120, an output module 130, a memory 140, and a processor 150. It can contain one. In some embodiments, at least one of the components of the electronic device 100 may be omitted, or one or more other components may be added to the electronic device 100.

The detector 110 can capture images of a sample. At this time, the detector 110 is installed at a predetermined location of the electronic device 100 and can capture an image. For example, the detector 110 includes at least one of a scientific complementary metal-oxide-semiconductor (sCMOS) camera, a photo multiplier tube (PMT), or other equipment that can measure the intensity of light and express it as an image. can do.

The input module 120 may receive a command or data to be used for at least one of the components of the electronic device 100 from outside the electronic device 100. At this time, the input module 120 may include at least one of an input device or a receiving device. For example, the input device may include at least one of a microphone, mouse, or keyboard. In some embodiments, the input device may include at least one of touch circuitry configured to detect a touch or a sensor circuit configured to measure the intensity of force generated by the touch. The receiving device may include at least one of a wireless receiving device or a wired receiving device.

The output module 130 may provide information to the outside of the electronic device 100. At this time, the output module 130 may include at least one of a display device or a transmission device. For example, the display device may include at least one of a display, a hologram device, or a projector. In some embodiments, the display device may be implemented as a touch screen by being assembled with at least one of the touch circuit or the sensor circuit of the input module 120. The transmission device may include at least one of a wireless transmission device or a wired transmission device.

According to one embodiment, the receiving device and the transmitting device may be integrated into one communication module. The communication module may support communication between the electronic device 100 and an external device (not shown). This communication module may include at least one of a wireless communication module or a wired communication module. At this time, the wireless communication module may be comprised of at least one of a wireless receiving device or a wireless transmitting device. Additionally, the wireless communication module may support at least one of a long-distance communication method or a short-distance communication method. The short-range communication method may include, for example, at least one of Bluetooth, WiFi direct, or infrared data association (IrDA). The wireless communication module may communicate in a long-distance communication manner through a network, and the network may include, for example, at least one of a cellular network, the Internet, or a computer network such as a local area network (LAN) or a wide area network (WAN). You can. Meanwhile, the wired communication module may be comprised of at least one of a wired receiving device or a wired transmitting device.

The memory 140 may store at least one of programs or data used by at least one of the components of the electronic device 100. For example, the memory 140 may include at least one of volatile memory and non-volatile memory.

The processor 150 may execute a program in the memory 140 to control at least one of the components of the electronic device 100 and perform data processing or calculation. The processor 150 may acquire a plurality of images, for example, n, for a plurality of, for example, n fluorescent substances each labeled with different biomolecules. At this time, the processor 150 may acquire a plurality of images in different detection wavelength bands that are distinct from each other. In each of the detection wavelength bands, the emission spectra of at least two of the fluorescent substances may overlap. Here, n may be a number of 3 or more. In other words, the processor 150 may acquire three or more images for three or more fluorescent substances. And, for each pair consisting of two of the acquired images, the processor 150 reduces the amount of mutual information shared between each image of the pairs, while reducing the amount of mutual information shared between each image of the pairs. Images can be separated.

). 이 때, 각 이미지별로 여러 형광 물질의 이미지가 선형적으로 더해지는 변수(α)는 다르게 된다. 검출 파장대를 선정함에 있어, 각 파장대별로 하나의 형광 물질의 방출 스펙트럼의 고점을 포함하도록 설계된다. 검출 파장대별로 하나의 형광 물질의 고점을 포함함이 보장될 경우, 이 이미지에서 다른 나머지 n-1 개의 이미지를 선형적으로 감하는 연산을 수행할 경우, 해당 대역에서 고점을 가지는 형광 물질에 비해 다른 분자로 인한 성분이 우선적으로 제거됨이 보장된다. 모든 m과 k에 대해 α_km, IMG_ch,m, IMG_F,k, 모두 양의 실수를 유지하는 범위 내에서 해당 성분의 크기를 정확히 추론하여 제거하기 위해, 도 2에 도시된 바와 같은 이중 루프 구조의 반복 업데이트 알고리즘(iterative update algorithm)이 제시된다.For example, if n images of different detection wavelengths are obtained by simultaneously exciting n fluorescent substances with one laser, the images of n types of fluorescent substances are linearly added to each image.

). At this time, the variable (α) to which the images of various fluorescent substances are linearly added is different for each image. When selecting a detection wavelength band, each wavelength band is designed to include the high point of the emission spectrum of one fluorescent material. If it is guaranteed to include the high point of one fluorescent material in each detection wavelength band, if an operation is performed to linearly subtract the remaining n-1 images from this image, the It is ensured that molecular components are preferentially removed. For all m and k, α _km , IMG _ch,m , and IMG _{F,k ,} all within the range of keeping positive real numbers, accurately infer and remove the magnitude of the corresponding components, a double loop as shown in Figure 2. An iterative update algorithm of the structure is presented.

The processor 150 selects two of the n images, and calls the two images X and Y (step i). Then, the processor 150 finds α that can minimize the mutual information amount I (X; Y - αX) of X and Y - αX and updates Y as shown in Equation 1 below (step ii). Here, the processor 150 calculates the mutual information amount, and updates use downsampling and unquantized Y. The processor 150 may downsample and quantize the acquired images, if necessary, before calculating the mutual information amount.

Here, ζ represents the update rate and is a real number between 0 and 1.

At this time, the processor 150 selects two of the n images according to all possible permutation combinations ( _n P ₂ ) and repeats the above processes (steps i and step ii) (step iii). Here, the processor 150 repeats the above processes (step i, step ii, and step iii) a predetermined number of times (p). At this time, the number of times (p) is set as a positive integer that satisfies the following [Equation 2] according to the target accuracy (ε) and update rate (ζ).

FIG. 3 is an exemplary diagram for explaining an example operation of the electronic device 100 according to various embodiments of the present disclosure. In FIG. 3, (a) is a graph of an example of the detection wavelength band detected by the electronic device 100. In each detection wavelength band, three fluorescent substances (CF488, ATTO514, ATTO532) excited using only a 488 nm laser are displayed. The emission spectra are superimposed, (b) is the staining image of NeuN guinea pig antibody and GFAP rat antibody control, (c) is the image before signal separation obtained at three detection wavelengths, (d) is the image after signal separation, Red represents PV, green represents NeuN, and blue represents GFAP.

Referring to Figure 3, in order to verify the effectiveness of the iterative update algorithm, three proteins, NeuN, GFAP, and PV, inside rat brain slices were labeled with three fluorescent substances with overlapping emission spectra, and the signals were separated using the iterative update algorithm. . To confirm that the separated image actually contained only the signal of one fluorescent substance, NeuN and GFAP were simultaneously stained with fluorescent substances of different wavelengths. The two images in (b) are images of NeuN and GFAP labeled with fluorescent substances of different wavelengths. Afterwards, signals of three fluorescent substances with overlapping emission spectra were obtained in the three detection wavelength bands shown in (a). As shown in (c), all three images obtained at three detection wavelengths contained three proteins (NeuN, GFAP, and PV). The separation result of applying the iterative update algorithm of PICASSO v3.0 to these three images is shown in (d). Comparing the images of channels 2 and 3 in (d) with the two images in (b), it can be seen that two of the three images separated using PICASSO v3.0 technology contain only NeuN and GFAP, respectively. In this way, the three proteins could be clearly distinguished in the signal-separated image using PICASSO v3.0. Through this, it was demonstrated that PICASSO v3.0 has improved multimolecular fluorescence imaging capabilities compared to PICASSO v1.0 and v2.0. Using PICASSO v3.0, three or more fluorescent materials can be used simultaneously in one wavelength band, and by adding a large Stokes shift fluorescent material, four or more fluorescent materials can be used simultaneously. Therefore, when PICASSO v3.0 is applied to the five commonly used wavelength bands, a total of 20 or more fluorescent substances can be used simultaneously.

FIG. 4 is a flowchart of a multi-color separation method through minimizing the amount of mutual information of the electronic device 100 according to various embodiments of the present disclosure.

Referring to FIG. 4 , in step 410, the electronic device 100 may acquire a plurality, for example, n images of a plurality of, for example, n fluorescent substances each labeled with different biomolecules. At this time, the processor 150 may acquire a plurality of images in different detection wavelength bands that are distinct from each other. In each of the detection wavelength bands, the emission spectra of at least two of the fluorescent substances may overlap. Here, n may be a number of 3 or more. In other words, the processor 130 may acquire three or more images for three or more fluorescent substances.

Next, the electronic device 100 reduces the amount of mutual information shared between each image of the pairs for each pair consisting of two of the images acquired in step 420, while reducing the amount of mutual information shared between the images of the pairs. Images for each fluorescent substance can be separated. To this end, the processor 150 is configured to perform an operation to obtain a plurality of new images that are updated with the acquired images by applying a variable (α) for minimizing the amount of mutual information calculated for each image of the pair. It can be. As a result, the acquired images can be obtained as images for each of the fluorescent substances. At this time, the processor 150 may perform this operation according to a predetermined update rate (ζ). In some embodiments, this operation may be repeated a predetermined number of times (p). When the repetition of this operation is completed, the processor 150 can acquire the finally obtained images as images for each of the fluorescent substances. For example, this operation may be repeated according to an iterative update algorithm with a double loop structure as shown in FIG. 2. This will be described in more detail later with reference to FIG. 5 .

FIG. 5 is a flowchart specifically illustrating the step 420 of separating images for each fluorescent material from the acquired images of FIG. 4 .

Referring to FIG. 5 along with FIG. 2 , the electronic device 100 may determine pairs each consisting of two (X, Y) among the n acquired images in step 521. At this time, the processor 150 may determine pairs according to all possible permutation combinations ( _n P ₂ ) from the n acquired images.

The electronic device 100 may calculate the variable α for each pair in step 523. At this time, the processor 150 may calculate the amount of mutual information for each pair based on the images (X, Y) of each pair. Here, the amount of mutual information can be defined as follows [Equation 3]. Through this, the processor 150 can calculate the variable α from the calculated amount of mutual information. In other words, the processor 150 can calculate the amount of mutual information from the images (X, Y). The processor 150 may downsample and quantize the acquired images, if necessary, before calculating the mutual information amount.

The electronic device 100 may apply the variable α to each of the pairs in step 525 to obtain new images (X, Y) that are updated with the acquired images (X, Y). At this time, the processor 150 applies the update rate (ζ) along with the variable (α) to one (Y) of each image (X, Y) of the pair, as shown in [Equation 4] below, For each of the images (X, Y), one (Y) of the images (X, Y) can be updated. Here, the processor 150 may update the acquired images (Y), that is, high-resolution images (Y) that are not downsampled and quantized.

In the above description, steps 523 and 525 are executed for each of the pairs determined in step 521, but are not limited thereto. That is, the result of a new image updated by performing steps 523 and 525 for one pair can be used in steps 523 and 525 of another pair. For example, given three acquired images, IMG1, IMG2, and IMG3, in step 521, six pairs are created, namely (IMG1, IMG2), (IMG2, IMG1), (IMG1, IMG3), (IMG3). , IMG1), (IMG2, IMG3), and (IMG3, IMG2) can be determined. In this case, steps 523 and 525 are first performed for (IMG1, IMG2), IMG1 is updated, and IMG1' can be obtained. Through this, (IMG2, IMG1), (IMG1, IMG3), and (IMG3, IMG1) can be converted to (IMG2, IMG1'), (IMG1', IMG3), and (IMG3, IMG1'), respectively. In this way, steps 523 and 525 may be performed simultaneously for all pairs, or steps 523 and 525 may be performed sequentially for each of the pairs, such that the result of steps 523 and 525 for one of the pairs is one of the pairs. It can be utilized for steps 523 and 525 on the other. And, after sequential progress, it can proceed to step 527.

The electronic device 100 may determine whether to repeat previous steps in step 527. At this time, the processor 150 may determine whether the number of repetitions of steps 521 to 525 has reached a predetermined number (p). Here, if it is determined that the number of repetitions has not reached the set number (p), the processor 150 determines that the previous steps must be repeated, returns to step 521, and repeats steps 521 to 525. there is. Meanwhile, if it is determined that the number of repetitions has reached the predetermined number (p), the processor 150 determines that there is no need to repeat the previous steps and may proceed to step 529. Here, the number of times (p) can be set as a positive integer that satisfies the target accuracy (ε) and update rate (ζ) as shown in [Equation 5] below.

Through this, the electronic device 100 can acquire the images finally obtained in step 529 as images for each of the fluorescent substances.

According to the present disclosure, image separation performance can be improved. At this time, in the present disclosure, signals of three or more fluorescent materials whose emission spectra overlap in one wavelength band can be separated. Specifically, in the present disclosure, without measuring the emission spectra of each fluorescent material, images for each fluorescent material can be separated only from images obtained in the same number of detection wavelength bands as the number of fluorescent materials.

The multicolor separation method by minimizing the amount of mutual information of the electronic device 100 according to the present disclosure includes the steps of acquiring a plurality of images for a plurality of fluorescent substances each labeled with different biomolecules (step 410), and For each pair consisting of two of the acquired images, images for each of the fluorescent substances are generated from the acquired images, while reducing the amount of mutual information shared between each image (X, Y) of the pairs. It may include a separation step (step 420).

According to the present disclosure, the step of acquiring a plurality of images (step 410) may include acquiring a plurality of images each in different detection wavelength bands that are distinct from each other.

According to the present disclosure, in each of the detection wavelength bands, the emission spectra of at least two of the fluorescent substances may overlap.

According to the present disclosure, the step of separating images for each of the fluorescent substances (step 420) is to apply a variable (α) for minimizing the amount of mutual information calculated for each image (X, Y) of the pair, and acquiring a plurality of new images that are updated with the acquired images, so that the acquired images can be obtained as images for each of the fluorescent substances.

According to the present disclosure, the step of acquiring a plurality of new images that are updated with the acquired images may be configured to acquire a plurality of new images that are updated with the acquired images according to a predetermined update rate (ζ). .

According to the present disclosure, the step of acquiring a plurality of new images updated with the acquired images is repeated a predetermined number of times (p), and when the repetition of the step of acquiring new images from the acquired images is completed, Images can be obtained for each of the fluorescent substances.

According to the present disclosure, the number p can be determined based on the target accuracy and update rate for images for each of the fluorescent materials.

According to the present disclosure, the number of times (p) can be determined as shown in [Equation 6] below.

Here, p represents the number of times, ε represents the target accuracy, and ζ represents the update rate, and the update rate may be a real number between 0 and 1.

According to the present disclosure, the step of acquiring a plurality of new images that are updated with the acquired images includes calculating a variable (α) from each of the images (X, Y) of the pairs (step 523), and A step (step 525) of updating one (Y) of each image (X, Y) of the pair by applying a variable (α) to one (Y) of each image (X, Y). You can.

The electronic device 100 according to the present disclosure may include a memory 140 and a processor 150 connected to the memory 140 and configured to execute at least one command stored in the memory 140.

According to the present disclosure, the processor 150 acquires a plurality of images for a plurality of fluorescent substances each labeled with different biomolecules, and for each pair consisting of two of the acquired images. , may be configured to separate images for each of the fluorescent substances from the acquired images, while reducing the amount of mutual information shared between each image (X, Y) of the pair.

According to the present disclosure, the processor 150 may be configured to each acquire a plurality of images at different detection wavelength bands distinct from each other.

According to the present disclosure, in each of the detection wavelength bands, the emission spectra of at least two of the fluorescent substances may overlap.

According to the present disclosure, the processor 150 applies a variable (α) for minimizing the amount of mutual information calculated for each pair of images (X, Y) to generate a plurality of new images that are updated with the acquired images. It is configured to perform an operation of acquiring the fluorescent substances, and the obtained images can be obtained as images for each of the fluorescent substances.

According to the present disclosure, the processor 150 may be configured to perform an operation according to a predetermined update rate (ζ).

According to the present disclosure, the processor 150 is configured to repeat the operation a predetermined number of times (p), and when the repetition of the operation is completed, the acquired images can be obtained as images for each of the fluorescent materials. there is.

According to the present disclosure, the number of times (p) can be determined based on the target accuracy (ε) and update rate (ζ) for images for each of the fluorescent materials.

According to the present disclosure, the number of times (p) can be determined as shown in [Equation 7] below.

Here, p represents the number of times, ε represents the target accuracy, and ζ represents the update rate, and the update rate may be a real number between 0 and 1.

According to the present disclosure, the processor 150 calculates a variable (α) from each of the images (X, Y) of the pair, and assigns the variable ( may be configured to update one (Y) of each pair of images (X, Y) by applying α).

The embodiments of the present disclosure described above are not only implemented through devices and methods, but may also be implemented through programs that implement functions corresponding to the configurations of the embodiments of the present disclosure or recording media on which the programs are recorded.

Although the embodiments of the present disclosure have been described in detail above, the scope of the rights of the present disclosure is not limited thereto, and various modifications and improvements using the basic concept of the present disclosure defined in the following claims are also included in the scope of the rights of the present disclosure. belongs to

Claims (20)

In the method of the electronic device,
Acquiring a plurality of images for a plurality of fluorescent substances each labeled with a different biomolecule; and
For each of the pairs, each consisting of two of the acquired images, separating the images for each of the fluorescent substances from the acquired images, while reducing the amount of mutual information shared between the images of each of the pairs. steps to do
Including,
The step of separating images for each of the fluorescent substances is,
Obtaining a plurality of new images updated with the acquired images by applying a product of a variable for minimizing the amount of mutual information calculated for each image of the pair and a predetermined update rate
Includes, whereby the acquired images are obtained as images for each of the fluorescent substances,
method.
According to claim 1,
The step of acquiring the plurality of images is,
Acquiring each of the plurality of images in different detection wavelength bands distinct from each other.
Including,
method.
According to claim 2,
In each of the above detection wavelength bands,
wherein the emission spectra of at least two of the fluorescent substances overlap,
method.
delete delete According to claim 1,
The step of acquiring a plurality of new images updated with the acquired images,
Repeated a predetermined number of times,
When the repetition of acquiring new images from the acquired images is completed, the acquired images are obtained as images for each of the fluorescent substances,
method.
According to claim 6,
The number of times is,
determined based on the target accuracy and the update rate for images for each of the fluorescent materials,
method.
According to claim 7,
The number of times is,
Determined as in [Equation i] below,
[Equation i]

Here, p represents the number of times, ε represents the target accuracy, and ζ represents the update rate, and the update rate is a real number between 0 and 1.
method.
According to claim 1,
The step of acquiring a plurality of new images updated with the acquired images,
calculating the variable from each image of the pairs; and
updating one of the respective images of the pair by applying the product of the variable and the update rate to one of the respective images of the pair.
Including,
method.
In electronic devices,
Memory; and
a processor coupled to the memory and configured to execute at least one instruction stored in the memory;
The processor,
Obtaining multiple images for multiple fluorescent substances each labeled with different biomolecules,
For each of the pairs, each consisting of two of the acquired images, separating the images for each of the fluorescent substances from the acquired images, while reducing the amount of mutual information shared between the images of each of the pairs. It is configured to
The processor,
Configured to perform an operation of acquiring a plurality of new images updated with the acquired images by applying a product of a variable for minimizing the amount of mutual information calculated for each image of the pair and a predetermined update rate,
As a result, the acquired images are obtained as images for each of the fluorescent substances,
Electronic devices.
According to claim 10,
The processor,
configured to each acquire the plurality of images at different detection wavelength bands distinct from each other,
Electronic devices.
According to claim 11,
In each of the above detection wavelength bands,
wherein the emission spectra of at least two of the fluorescent substances overlap,
Electronic devices.
delete delete According to claim 10,
The processor,
Configured to repeat the operation a predetermined number of times,
When the repetition of the operation is completed, the obtained images are obtained as images for each of the fluorescent substances,
Electronic devices.
According to claim 15,
The number of times is,
determined based on the target accuracy and the update rate for images for each of the fluorescent materials,
Electronic devices.
According to claim 16,
The number of times is,
Determined as in [Equation ii] below,
[Equation ii]

Here, p represents the number of times, ε represents the target accuracy, and ζ represents the update rate, and the update rate is a real number between 0 and 1.
Electronic devices.
According to claim 10,
The processor,
Calculate the variable from each image of the pair,
configured to update one of the respective images of the pair by applying a product of the variable and the update rate to one of the respective images of the pair,
Electronic devices.
1. A non-transitory computer-readable storage medium storing one or more programs for executing a method on an electronic device, comprising:
The above method is,
Acquiring a plurality of images for a plurality of fluorescent substances each labeled with a different biomolecule; and
For each of the pairs, each consisting of two of the acquired images, separating the images for each of the fluorescent substances from the acquired images, while reducing the amount of mutual information shared between the images of each of the pairs. steps to do
Including,
The step of separating images for each of the fluorescent substances is,
Obtaining a plurality of new images updated with the acquired images by applying a product of a variable for minimizing the amount of mutual information calculated for each image of the pair and a predetermined update rate
Includes, whereby the acquired images are obtained as images for each of the fluorescent substances,
Non-transitory computer-readable storage media. delete