Computer-implemented method and data processing apparatus for extracting signal using crosstalk amount

Abstract

The present invention aims to provide a data processing apparatus and a computer-implemented method for calculating an estimate of a first signal of a plurality of signals, the plurality of signals contained in a digital color input image, each signal of the plurality of signals having a different reference true spectrum. The method comprises the steps of: retrieving an appropriate subset from a plurality of pixels of a digital color input image by a data processing device (420) configured to perform the steps of: retrieving an appropriate subset from the plurality of pixels of the digital color input image; extracting a preliminary estimate of the first signal from the subset by spectral unmixing as a first unmixed signal and a preliminary estimate of at least one other signal of the plurality of signals as at least one other unmixed signal; calculating an estimate of a dependency of the first demixed signal on at least one other signal as a crosstalk amount of the subset; a crosstalk amount is removed from the first demixed signal to obtain an estimate of the first signal in the subset. The data processing device may be included in an optical observation device such as a microscope, in particular a laboratory or surgical microscope.

Description

Technical Field

[0001] The present invention generally relates to image processing using computer-implemented methods and / or data processing apparatus. The present invention also relates to an optical observation apparatus, such as a microscope or an endoscopic apparatus, comprising the data processing apparatus, and a method of using such an optical observation apparatus, including the computer-implemented method. Background Art

[0002] In image processing, it is often necessary to extract one or more signals from an image, in particular a digital color image. For example, these signals may correspond to the fluorescence emission of different fluorophores and / or fluorophores in different chemical environments in an object (such as a biological tissue, for example a cell, a part of a cell or a part of an organism). Fluorophores are often used as markers, which accumulate in areas of biological tissue with specific properties. Other fluorophores are transported in blood vessels and used to mark channels. Some fluorophores are naturally present in the object being observed. The type of biological tissue marked depends on the chemical properties of the fluorophore. Other signals may not be generated by fluorescence emission, but by light passing through the object or reflected from the object.

[0003] More than one fluorophore may be present in a given region of biological tissue. Furthermore, different fluorophores may be contained in separate regions that overlap along the optical axis, so that they appear to be located in the same part of the object.

[0004] In each of these cases, the signals may not only overlap spatially, but more importantly, spectrally as well, and therefore must be separated from each other as accurately as possible. Different fluorophores may have overlapping fluorescence emission spectra, or the fluorescence emission spectrum of one fluorophore may overlap with the fluorescence excitation spectrum of one or another fluorophore.

[0005] Therefore, there is a need to provide a computer-implemented method and a data processing device that can accurately separate at least one signal contained in a digital color input image from at least one other signal. Summary of the Invention

[0006] The above needs are solved by providing a computer-implemented method for calculating an estimate of a first signal among a plurality of signals, wherein the plurality of signals are contained in a digital color input image, each of the plurality of signals having a different reference true spectrum, and the digital color input image comprising a plurality of pixels; wherein the computer-implemented method comprises the following steps: extracting an appropriate subset of the plurality of pixels from the digital color input image; extracting a preliminary estimate of the first signal from the subset as a first unmixed signal by spectral unmixing, and extracting a preliminary estimate of at least one other signal among the plurality of signals as at least one other unmixed signal; calculating an estimate of the dependence of the first unmixed signal on the at least one other signal from the subset as a crosstalk amount for the subset; and removing the crosstalk amount from the first unmixed signal of the subset to obtain an estimate of the first signal in the subset.

[0007] The above-mentioned requirements can also be solved by a data processing device, which is used to calculate an estimated value of a first signal among multiple signals, wherein the multiple signals are contained in a digital color input image, each of the multiple signals has a different reference real spectrum, and the digital color input image includes multiple pixels; wherein the data processing device is configured to: retrieve an appropriate subset of the multiple pixels in the digital color input image; extract a preliminary estimated value of the first signal from the subset as a first unmixed signal by spectral unmixing, and extract a preliminary estimated value of at least one other signal among the multiple signals as at least one other unmixed signal; calculate an estimated value of the dependence of the first unmixed signal on the at least one other signal as the crosstalk amount of the subset; and remove the crosstalk amount from the first unmixed signal to obtain an estimated value of the first signal in the subset.

[0008] In the digital color input image, the signals are superimposed on one another, so they can only be separated computationally. The computer-implemented method and data processing apparatus described above improve the accuracy of the estimate of the first signal (i.e., the unmixed signal) by using a crosstalk measure. The crosstalk measure reflects the dependence of the first unmixed signal on at least one other unmixed signal. This is already an improvement over known spectral unmixing methods. Furthermore, dividing the digital color input image into appropriate subsets and calculating the crosstalk measure only for the subsets, rather than for the entire image, constitutes a further improvement.

[0009] The above method can be further improved by the following features, which are independent of each other and can be combined in any combination, wherein each feature has its own technical effect. These features are applicable to both the method and the device without exception, regardless of whether they are described in the context of the method or the device.

[0010] In one example, the digital color input includes or can be generated using multiple color channels, or synonymously referred to as color space coordinates. The digital color input image can be represented in any color space, for example, it can be a tristimulus, multispectral or hyperspectral image. Each color channel itself can be regarded as a (monochrome) image. The same situation may also apply to the pixels of the digital color input image, which can be color pixels represented in any color space and / or can be multispectral or hyperspectral pixels. Therefore, each pixel can contain multiple color channels. In this subset, at least two of the multiple signals can contain at least one identical color space coordinate.

[0011] Each signal is represented by a color space coordinate, or more precisely, a tuple of color space coordinates. For example, in the RGB color space, each signal is represented by three color space coordinates, which represent the intensity of the corresponding signal in the red, green, and blue channels. When more than one signal exists at an image location (such as a subset, or more specifically, a pixel), the color space coordinates reflect the superposition of these signals.

[0012] Preferably, the number of color channels or color space coordinates of the digital color input image is at least as many as the number of signals to be extracted or estimated.

[0013] The appropriate subset extracted from the digital color input image may correspond to a connected region of the digital input image, but not necessarily a simply connected region. The subset may also correspond to a disjoint region of the digital color input image. The subset may consist of one or more pixels. The pixels of the subset may be continuous and / or discontinuous.

[0014] In one embodiment, all pixels in the subset may have at least one color appearance parameter within the same predefined and preferably user-controllable range. Pixels in a different subset may have at least one color appearance parameter within a different predefined and preferably user-controllable range. Color appearance parameters may include any of hue, lightness, brightness, chroma, color richness, and saturation.

[0015] Alternatively or cumulatively, the pixels of the subset may be located within a predetermined neighborhood, i.e., within a determined maximum distance from one another. Gathering pixels in the subset based on position and / or at least one color appearance parameter can improve the accuracy of the estimate. For example, the subset may include pixel blocks of predetermined, but preferably user-controllable, sizes, such as 2x2, 3x3, 2x3, 4x4, etc.

[0016] The digital color input image can be considered as a digital multiplexed color input image, which includes one or more other signals in addition to the first signal, wherein each of the one or more other signals in the plurality of signals has a respective ground truth spectrum that is different from the ground truth spectrum of all other signals, particularly including the first signal. The term "ground truth" refers to a signal (or its spectrum) recorded in an ideal artifact-free recording process. Therefore, although the signals in the digital color input image represent the respective ground truth signals, they are only approximations of the respective ground truth signals.

[0017] One application of a data processing device and / or computer-implemented method is processing fluorescence images. For example, the first signal may correspond to a first fluorescence signal having a first spectrum. The first signal may be generated by, or represent, fluorescence emission from a first fluorophore. The first signal may have a first spectrum representing a first fluorescence emission spectrum, i.e., the fluorescence emission spectrum of the first fluorophore.

[0018] The multiple signals in the digital color input image may include two or at least two signals, such as three or four signals. Some or all of the signals may represent fluorescence emissions from different fluorophores, each of which has a different fluorescence spectrum. Furthermore, some or all of the signals may represent different fluorescence spectra of the same fluorophore under different chemical environments. The fluorescence spectra of some fluorophores may vary significantly depending on the chemical environment.

[0019] According to another aspect, the plurality of signals may include one or more signals representing an illumination spectrum of the observed object. The illumination spectrum may consist of or include a fluorescence excitation spectrum of a fluorophore, the fluorescence of which is represented in one signal or in multiple signals. According to another aspect, the illumination spectrum may include a fluorescence emission spectrum of a fluorophore represented by one of the plurality of signals, be included in a fluorescence emission spectrum of a fluorophore represented by one of the plurality of signals, or overlap with a fluorescence emission spectrum of a fluorophore represented by one of the plurality of signals. According to yet another aspect, the illumination spectrum may include a fluorescence excitation spectrum of a fluorophore represented by a certain signal in the plurality of signals, be included in a fluorescence excitation spectrum of a fluorophore represented by a certain signal in the plurality of signals, or overlap with a fluorescence excitation spectrum of a fluorophore represented by a certain signal in the plurality of signals. Some, some, or all of the signals may represent a transmission spectrum; some, some, or all of the signals may represent a reflection spectrum.

[0020] Extracting a subset and subsequently extracting a first unmixed signal and at least one other unmixed signal, calculating the amount of crosstalk of the subset from the subset, and removing the amount of crosstalk from the first unmixed signal to obtain an estimated value of the first signal in the subset can be repeated for different subsets until the extracted subset combination covers a predetermined portion, in particular all portions, of the digital color input image.

[0021] In such an embodiment, it is preferred that the different subsets do not overlap. However, if the subsets overlap, the average crosstalk amount can be calculated for the overlapping subsets.

[0022] In a preferred embodiment, each subset contains only a single pixel. The amount of crosstalk is then calculated for each pixel individually and removed from the preliminary estimate of the unmixed signal, as described above and / or below. In this way, all pixels of the digital color input image or all pixels of a predetermined and preferably user-selectable region of the digital color input image can be processed, without these pixels being necessarily connected.

[0023] Because the unmixed signal is represented by its color space coordinates in the color space of the digital color input image, the crosstalk amount can also be represented by color space coordinates, i.e., as a tuple of color space coordinates. Each color space coordinate of the crosstalk amount represents the dependence of the color space coordinate of the preliminary estimate of one signal on the same color space coordinate of the preliminary estimate of another signal. If the color space of the digital color input image is not suitable for calculating the estimate, a conversion to another color space can be performed, and all calculations can be performed in that color space.

[0024] The estimated value of the first signal (and any other signals) does not have to be a mathematically optimal estimate. From an application perspective, it may be more preferable to set the estimated value to zero if the amount of crosstalk exceeds a predetermined threshold, as in this case the estimated value may be too unreliable to serve as a basis for further post-processing.

[0025] The amount of crosstalk will vary from subset to subset because it depends on the subset.

[0026] Hereinafter, if an additional crosstalk amount is calculated for at least one other signal, the crosstalk amount representing the estimated value of the dependence of the first unmixed signal on the at least one other signal is referred to as a “first crosstalk amount”.

[0027] As described above, the crosstalk amount represents the influence of one or more additional signals on another signal. Therefore, a plurality of different crosstalk amounts can be calculated for the subset. Each of these crosstalk amounts preferably represents the dependence of a preliminary estimate of one signal on a preliminary estimate of another signal.

[0028] According to another aspect, the step of extracting a preliminary estimate of at least one other signal from the plurality of signals as at least one other unmixed signal from the subset by spectral unmixing may include the following steps: extracting a preliminary estimate of a second signal from the plurality of signals as a second unmixed signal by spectral unmixing. Here, the at least one other signal corresponds to the second signal. An estimate of the dependence of the second unmixed signal on the first unmixed signal may be calculated as a second crosstalk amount. The second crosstalk amount may be removed from the second unmixed signal to obtain an estimate of the second signal in the subset.

[0029] This aspect allows for extracting an estimate of the first signal and an estimate of the second signal with greater accuracy. The second signal can correspond to a second fluorescence signal having a second spectrum, wherein the second spectrum represents a second fluorescence emission spectrum. However, the first signal and the second signal do not necessarily correspond to fluorescence signals, but can instead correspond to different reflection signals and / or fluorescence excitation signals of different fluorophores.

[0030] For example, the first signal may represent a first fluorescence emission spectrum of a first fluorophore, and the second signal may represent a second fluorescence emission spectrum of a second fluorophore, the first fluorophore being different from the second fluorophore. Alternatively, the first signal may represent the first fluorescence emission spectrum of the first fluorophore in a first chemical environment, and the second signal may represent the second fluorescence emission spectrum of the first fluorophore in a second chemical environment, the second chemical environment being different from the first chemical environment. Again, alternatively, the first signal may represent a fluorescence emission spectrum, and the second signal may represent a fluorescence excitation spectrum. To trigger fluorescence emission, the object of interest may be illuminated with light consisting of or including the fluorescence excitation spectrum.

[0031] Similar to the above steps, other crosstalk quantities may be used to calculate an estimated value of the third unmixed signal in the subset.

[0032] For example, the step of extracting a preliminary estimate of at least one other signal from a plurality of signals in a subset as at least one other unmixed signal by spectral unmixing may include the following steps: extracting a preliminary estimate of a third signal from a plurality of signals in a subset as a third unmixed signal by spectral unmixing. A third crosstalk amount may be calculated, which is an estimate of the dependence of the third unmixed signal in the subset on the first unmixed signal and / or a fourth crosstalk amount may be calculated, which is an estimate of the dependence of the third unmixed signal in the subset on the second unmixed signal. The third crosstalk amount and / or the fourth crosstalk amount may be removed from the third unmixed signal to obtain an estimate of the third signal in the subset. In addition, a fifth crosstalk amount may be calculated, which is an estimate of the dependence of the first unmixed signal on the third unmixed signal. The first crosstalk amount and the fifth crosstalk amount may be removed from the first unmixed signal to obtain an estimate of the first signal in the subset. This further improves the accuracy of the estimate of the first signal.

[0033] To improve the accuracy of the second signal, a sixth crosstalk amount can be calculated, which is an estimate of the dependence of the second unmixed signal on the third unmixed signal. The second and sixth crosstalk amounts can be removed from the second unmixed signal to obtain an estimate of the second signal in the subset.

[0034] The third signal can represent a third fluorescence signal of a third fluorophore that is different from the first and second fluorophores. As with the second signal, the third signal can represent a fluorescence emission spectrum or fluorescence excitation spectrum, or reflected or transmitted light, of the first, second, or third fluorophores. According to another aspect, the third signal can represent a third fluorescence emission spectrum of the first fluorophore or a third fluorescence emission spectrum of the third fluorophore in a third chemical environment that is different from the first or second chemical environment.

[0035] The digital color input image can represent a color image of an observed object, which is primarily composed of biological matter or tissue. The colors represented in the digital color input image can extend beyond the human visible spectrum, i.e., include wavelengths below and / or above the visible spectrum, i.e., include wavelengths below 380 nm and / or above 750 nm. The digital color input image preferably represents an imaging spectrum (or, synonymously, an imaged spectrum) that overlaps with, is contained within, or includes the fluorescence spectrum of at least one fluorophore contained in the observed object. This ensures that the fluorescence of interest is captured.

[0036] In one embodiment, the step of extracting by spectral unmixing can include one of the following: extracting by linear spectral unmixing and extracting by non-negative tensor decomposition. Linear spectral unmixing and non-negative tensor decomposition are less computationally expensive in most cases while still providing accurate results. In this context, a "matrix" is considered a special two-dimensional case of a tensor. In other words, a tensor is an n-dimensional matrix, where n>1.

[0037] In particular, the step of extracting by spectral unmixing may comprise solving a set of equations representing a linear superposition of the plurality of signals in the subset. The set of equations may be solved using a minimization routine (e.g., a minimization routine obtained from a software library).

[0038] The individual reference true spectra of the first signal and, if applicable, the second, third, and any other signals are preferably used as endmembers for spectral unmixing. The contribution of each endmember to the total spectrum recorded in the subset or digital color input image, respectively, is called the abundance. The endmembers can be determined experimentally, for example, by measuring the fluorescence emission spectrum of the fluorophore with very high accuracy, preferably in the color space of the digital color input image.

[0039] In spectral unmixing, in particular linear spectral unmixing, the step of extracting preliminary estimates of the first signal and (if applicable) the second, third and / or other signals contained in the subset may include the step of calculating an estimate of a mixture tensor for the subset. The mixture tensor maps the reference truth of the first signal and (if applicable) the second, third and / or other signals to the corresponding first signal and (if applicable) the second, third and / or other signals in the subset, respectively. As described above, the signals in the subset are represented by color channel coordinates. Therefore, strictly speaking, the mixture tensor maps the reference truth of the signal to its (recorded) representation in color space. The mixture tensor is calculated separately for each subset.

[0040] In another embodiment, the step of extracting preliminary estimates of the first signal and, if applicable, the second, third, or other signals contained in the subset may comprise the step of computing an estimate of the mixture tensor using a minimization method. In one example, the estimated value of the mixture tensor may be calculated by minimizing L n -norm to calculate the estimated value of the mixed tensor. In addition, the calculated estimated value of the mixed tensor can minimize the deviation between one, part or all of the signals contained in the subset and the result of linearly transforming the end members of each signal (multiple signals) by the estimated value of the mixed tensor. The deviation can be expressed as any L n norm, where n is a natural number.

[0041] The diagonal elements of the crosstalk tensor represent the contributions of the corresponding ground truth signals in the subset. For example, the first diagonal element of the crosstalk tensor may correspond to the contribution of the first signal in the subset, and the second diagonal element of the crosstalk tensor may correspond to the contribution of the second signal in the subset. The diagonal elements of the crosstalk tensor corresponding to the signals representing the fluorescence emission of the fluorophores can indicate the concentration of the corresponding fluorophores at the locations of the observed object, which are mapped into the subset.

[0042] In another embodiment, the step of calculating the amount of crosstalk may include calculating the probability that the first, second, third, and / or other unmixed signals correspond to the corresponding first, second, third, and / or other signals. In a specific embodiment, the amount of crosstalk may be calculated based on the mixing tensor, in particular based on the non-diagonal elements of the mixing tensor.

[0043] Alternatively or cumulatively, the step of calculating the amount of crosstalk may comprise calculating a correlation or covariance between one signal and the other signal. The covariance may be calculated from the correlation.

[0044] In particular, the amount of crosstalk can be calculated as the correlation or covariance between one of the multiple signals in the subset and another different signal in the multiple signals in the subset. In other words, the amount of crosstalk is calculated based on the correlation or covariance between the signal to be removed from the multiple signals in the subset and another signal in the multiple signals. In another method, a Bayesian estimator can be used to calculate the covariance. For example, the first amount of crosstalk can be calculated based on the correlation or covariance between the first signal and the third signal. The same applies to the second to sixth crosstalk amounts and any other crosstalk amounts.

[0045] Since matrix calculation is more efficient, in the step of calculating the crosstalk amount, a crosstalk tensor may be preferably calculated, wherein the crosstalk amount of the crosstalk tensor is an element, especially a non-diagonal element.

[0046] The dimensions of the crosstalk tensor can be determined by the number of unmixed signals used to calculate the crosstalk tensor. For example, if the crosstalk tensor is calculated based on two unmixed signals, the dimensions of the crosstalk tensor can be 2x2. If the crosstalk tensor is calculated based on three unmixed signals, the dimensions of the crosstalk tensor can be 3x3, and so on. In this case, each element of the crosstalk tensor can contain all the color space coordinates of the color space of the digital input image.

[0047] The step of calculating the crosstalk amount may include the step of calculating the crosstalk amount based on non-diagonal elements of the crosstalk tensor. In certain cases, the crosstalk amount may correspond to a non-diagonal element of the crosstalk tensor.

[0048] In one variant, the step of calculating the crosstalk tensor and / or the crosstalk amount may include calculating at least one of a covariance tensor and a Fisher information tensor based on the unmixed signal.

[0049] Calculating the covariance tensor may include calculating the covariance tensor based on weighted estimates of the mixture tensor. The weights may depend on the subset, i.e., they may be different for each different subset. For example, the weights may depend on the preliminary estimates of the first, second, third, and / or any other signals. The weights may consist of an average of the preliminary estimates of the signals in the subset, i.e., a vector that weights each column of the mixture tensor separately. The weights may also consist of the median of the preliminary estimates of the signals in the subset. The weights may also be a scalar calculated based on the preliminary estimates of the signals in the subset, e.g., a global average of the preliminary estimates of all signals in the subset.

[0050] To improve the unmixed signal in the subset, crosstalk can be removed in various ways. For example, removing crosstalk from the unmixed signal can include one of: subtracting the crosstalk from the unmixed signal; and multiplying the unmixed signal by the crosstalk. Crosstalk is preferably expressed in unmixed signal space, independent of any color space. Signal space is formed by using endmembers as "coordinates."

[0051] The crosstalk amount may be additively and / or multiplicatively weighted by a preferably user-controllable weight parameter.The weight parameter may be a scalar, in particular a constant for all signals in the subset.

[0052] The amount of crosstalk may depend on or correspond to the off-diagonal elements of the covariance tensor. In one specific example, the amount of crosstalk for an unmixed signal may be calculated by summing the off-diagonal elements of the covariance tensor that represent the correlation between the unmixed signal and another unmixed signal. A weight parameter may be applied to this sum.

[0053] The crosstalk amount can be directly subtracted from the corresponding unmixed signal. If the result of the subtraction is negative, all pixels in the subset can be set to zero. If, after subtracting the crosstalk amount, the individual signals become negative in signal space, all pixels in the subset can even be set to zero.

[0054] Another form of the crosstalk amount can be derived, for example, by using the optional weighted sum of the off-diagonal elements of the covariance matrix as the negative exponent of a number. Such a crosstalk amount represents a confidence value that decreases exponentially with the sum of the off-diagonal elements of the covariance tensor. Such a crosstalk amount can be used to multiply the corresponding signal in the subset. Therefore, if a certain unmixed signal is highly correlated with another unmixed signal, the unmixed signal will be further weakened.

[0055] The digital color input image can be retrieved from a storage device, such as a memory of a data processing device and / or an optical observation device. The storage device can be located in the cloud, on a disk, or in any other non-volatile memory. Alternatively, the digital color input image can be retrieved directly from a camera of the optical observation device.

[0056] According to another embodiment, separate digital color output images may be generated from the estimates of the first signal and, if applicable, the second signal and / or any other signals. Alternatively, all estimates may be combined into a single digital color output image.

[0057] The subset of the digital color input image used for calculating the estimate of the first signal and, if applicable, any other signal, is preferably located at the same position in the digital color output image as in the digital color input image.The digital color input image and the digital color output image are preferably registered with respect to each other.

[0058] The estimated values ​​of the first and / or any other signal, and / or the digital color output image can be present, for example, in a memory of the data processing device so as to be accessible, in particular from outside the digital processing device. Such access can, for example, be used to display the estimated value(s) and / or the digital color output image.

[0059] The present invention also relates to a computer program comprising instructions, which, when executed by a computer, causes the computer to execute the method in any of the above embodiments.

[0060] Furthermore, the invention relates to a computer-readable medium on which such a computer program is stored.

[0061] As mentioned above, the data processing apparatus may be configured to perform any of the steps or embodiments described above.

[0062] The computer-implemented method of any of the above embodiments may be part of a method using an optical observation device, such as a microscope or an endoscope. The microscope may be a laboratory microscope or a surgical microscope. Furthermore, the microscope may be a fluorescence microscope, such as a fluorescence surgical microscope or a fluorescence laboratory microscope. The method using the optical observation device may further include the steps of: recording at least one color image of the object under investigation; and generating a digital color input image based on the at least one color image.

[0063] The step of generating the color input image from the at least one color image may comprise computing a digital color input image from a plurality of different color images. Thus, the different color images may be combined into a single digital color input image.

[0064] In its simplest form, the digital color input image can be retrieved directly from the camera.

[0065] The optical observation device may comprise at least one camera, in particular at least one fluorescence camera, for recording the first signal and any further signals. The at least one camera of the optical observation device may be configured to record at least one color image, wherein the optical observation device is configured to generate the digital color input image from the at least one color image as described above.

[0066] In an advantageous embodiment, the data processing device may include or consist of an embedded system of the optical observation device.

[0067] For example, the embedded system can be configured to control an optical observation device, such as controlling an actuator for moving the observed object, for autofocus, for automatic tracking, for moving a lens to change the focus and / or focal length, for moving a microscope stage, for controlling the illumination of the observed object (such as the illumination spectrum and / or illumination brightness, etc.).

[0068] The optical observation device can include a graphical user interface in hardware and / or software. The graphical user interface can include interactive areas designed for user input. For example, in any of the above embodiments, user-controllable parameters used in the data processing device and / or computer-implemented method can be changed based on user interaction with the interactive area.

[0069] The interaction area can alternatively or cumulatively be used to control the optical viewing device.

[0070] As used herein, “and / or” includes any and all combinations of one or more of the associated listed items and may be abbreviated as “ / ”.

[0071] Although some aspects are described in the context of an apparatus, it is apparent that these aspects also represent a description of the corresponding method, where a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of the corresponding apparatus.

[0072] Hereinafter, with reference to the accompanying drawings, a solution to the above-mentioned problem is exemplarily described using embodiments of the present invention. In the drawings, the same reference numerals are used for elements that correspond to each other in terms of function and / or physical structure.

[0073] Furthermore, according to the above description, in a specific application, if the technical effect of an element described in the context of an embodiment is not essential, then the element may be omitted from the embodiment. Conversely, if an element is mentioned in the above description but not in the context of the embodiment below, but the technical effect associated with the element is advantageous in a specific application, then the element may be added to the embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1A A schematic diagram of the object being observed, including the signal source, is shown;

[0075] Figure 1B Shown Figure 1A a schematic diagram of a digital color input image of the object shown;

[0076] Figure 2 Shown for calculation Figure 1A and Figure 1B Schematic diagram of the method for the signal shown;

[0077] Figure 3 a schematic diagram illustrating the crosstalk effect; and

[0078] Figure 4 A schematic diagram of the optical observation setup is shown. DETAILED DESCRIPTION

[0079] exist Figure 1A , an object 120 is shown being viewed by an optical viewing device 134, such as a laboratory or surgical microscope or endoscope.

[0080] The object 120 preferably includes or consists of biological tissue. For example, the object 120 can be a cell, a part of a cell (such as a cell organelle), or a part of the trunk of an organism (such as an animal or a plant).

[0081] Object 120 includes one or more sources 122, 124, 126 of reference real signals 128, 130, 132, and optical observation device 134 is configured to record these signals. Reference real signals 128, 130, 132 are generated by at least one source 122, 124, 126 in the form of light. The source may include, overlap within, or be confined to the visible spectrum. For example, source 122, 124, 126 may be a fluorophore that emits fluorescence within a fluorescence emission spectrum. The fluorescence emission spectrum of each different fluorophore may be different, and / or the fluorescence emission spectrum of each different fluorophore may depend on the chemical environment in which the fluorophore is located. The fluorophore may be artificially added to object 120 or may be naturally present in object 120.

[0082] Alternatively or cumulatively, sources 122, 124, and 126 can be materials that reflect or transmit light, thereby illuminating object 120 with a specific transmission spectrum and / or reflection spectrum, respectively. Again alternatively or cumulatively, sources 122, 124, and 126 can be regions illuminated by an illumination spectrum that transmits, reflects, and / or triggers fluorescence. Any number of sources can be included in object 120. Only three sources have been selected as an example for ease of understanding.

[0083] Reference real signals 128, 130, 132 generated by at least one source 122, 124, 126 are recorded by an optical observation device 134 as signals 104, 106, 108 in the digital color input image 100, as shown in FIG. Figure 1B Any number of signals can be represented in the digital color input image 100. However, it is preferred that the number of color channels (or synonymously, color space coordinates) is at least as great as the number of signals or sources that are recorded in the digital color input image 100 and that need to be distinguished from one another.

[0084] The digital color input image 100 is a color image, such as a tristimulus image, a multispectral image, or a hyperspectral image. The digital color input image 100 includes multiple color channels (or, equivalently, color space coordinates). The digital color input image 100 can be represented using any color space. Each color space defines a tuple of color space coordinates, each of which defines a color. Different color space coordinates can be converted to one another. However, this conversion does not necessarily have to be bijective.

[0085] The digital color input image 100 may be generated based on a plurality of individual color images, which may be captured sequentially by a single camera in the optical observation device 134 or captured in parallel by a plurality of cameras.

[0086] A digital color input image 100 comprises a plurality of pixels 102. Each pixel 102 comprises a tuple of color space coordinates, which determine the color of the pixel 102. Each region of an object 120 is mapped to a different area.

[0087] The digital color input image 100 can be divided into appropriate subsets 118, each of which includes at least one pixel. Each subset 118 represents a region of the digital color input image 100, and therefore represents a region of the object 120. The subsets 118 can be connected, but not necessarily simply connected, regions, or disconnected regions of the digital color input image 100. Each subset 118 includes at least one pixel. In the simplest case, each subset 118 contains only a single pixel. When processing the digital color input image 100, these subsets 118 can be processed separately. Reference numerals 118a and 118b indicate different subsets 118, each of which includes at least one pixel 102.

[0088] The at least one signal 104, 106, 108 recorded in the digital color input image 100 is only an approximation of the corresponding at least one reference true signal 128, 130, 132 generated by the at least one source 122, 124, 126, since the recording process inevitably produces artifacts such as noise, distortion and further linear and / or nonlinear errors.

[0089] according to Figure 1B , a first signal 104 is recorded in the digital color input image 100, which represents a reference real signal 128 generated by a source 122. Further, a second signal 106 is recorded in the digital color input image 100, which represents a reference real signal 130 generated by a second source 124. Further, a third signal 108 may be recorded in the digital color input image 100, which represents a reference real signal 128 generated by a third source 126.

[0090] More than one source 122, 124, 126 may be present in some or all regions of object 120. In such regions, more than one reference true signal 128, 130, 132 is generated. Therefore, the region of object 120 mapped to this region of digital color input image 100 contains more than one signal 104, 106, 108. For example, first signal 104 and second signal 106 overlap in region 110 of digital color input image 100, first signal 104 and third signal 108 overlap in at least one region 112 of digital color input image 100, second signal 106 and third signal 108 overlap in at least one region 116 of digital color input image 100, and all three signals 104, 106, 108 overlap in region 114 of digital color input image 100. In regions 112, 114, 116, each color space coordinate is generated by the superposition and artifacts of at least two signals 104, 106, 108.

[0091] Due to physical processes occurring in the object 120 and / or during the recording process, the signals 104, 106, 108 in the digital color input image 100 may be affected by at least one other signal 104, 106, 108. For example, the signals 104, 106 may exhibit a linear or nonlinear dependency on each other, which may not be known a priori. The same applies to any combination of the other signals 104, 106, 108.

[0092] To separate signals 104, 106, and 108 from one another, linear or nonlinear unmixing is applied, depending on whether linear or nonlinear dependencies are assumed between signals 104, 106, and 108, respectively. Spectral unmixing is applied to each subset 118 separately, i.e., each subset 118 is processed independently. Spectral unmixing can be performed on multiple subsets 118 until the desired portion of digital color input image 100 is processed. The portion of digital color input image 100 to be processed can be controlled by the user. The processed subsets 118 can be overlapping or non-overlapping. Non-overlapping subsets 118 are preferred because they reduce the amount of numerical computation required.

[0093] If spectral unmixing is applied to the digital color input image 100, preliminary estimates 210, 212, 214 of the unmixed signals (ie signals 104, 106, 108) are obtained.

[0094] Subset 118 may be defined by pixels 102 that share a predetermined, and preferably user-controllable, range of values ​​for at least one color appearance parameter (e.g., hue, value, brightness, chroma, color richness, saturation, etc.). For example, subset 118 may be defined by pixels 102 where hue is within a predefined range of values ​​and brightness is also within a predefined range of values.

[0095] Additionally or cumulatively, the pixels 102 in the subset 118 may satisfy geometric constraints, e.g., they are no more than a predetermined and preferably user-controllable distance apart from each other, or cover an area no larger than a predetermined and preferably user-controllable area of ​​the digital color input image 100. In a very simple embodiment, the subset may comprise a square arrangement of pixels, e.g., 2x2 or 3x3 pixels.

[0096] In the following, linear spectral unmixing is used to exemplarily describe the determination of improved estimates of the signals 104, 106, 108. In this model, it is assumed that the signals 104, 106, 108 recorded in the digital color input image 100 are obtained by linear transformation of the reference true signals 128, 130, 132 generated by the sources 122, 124, 126. The linear transformation is obtained by the unknown mixing tensor M cd express:

[0097]

[0098] Here, y cp represents the color space coordinate c of the signal y recorded at the specific subset 118 with index p. The index d represents the index of the reference real signal 128, 130, 132. N d is the total number of reference true signals. d Denotes the reference true signals 128, 130, 132 with index d. The index d can be considered to represent the different sources 124, 126, 128. The dimension of the mixing tensor is N c ×N d , where N c is the number of color space coordinates in the color space at which spectral unmixing is performed.

[0099] Reference real signal x dp represents, for example, the spatial distribution of the fluorophore concentration of fluorophore d in each subset p.

[0100] The above equation can be solved, for example, by maximum likelihood estimation to obtain the unknown mixture tensor M, in particular by blind unmixing, linear unmixing or non-negative tensor decomposition.

[0101] For example, as described by Timo Zimmermann in "Spectral Imaging and Linear Unmixing in Light Microscopy," Adv Biochem Engin / Biotechnol (2005), Vol. 95, pp. 245-265, the mixing tensor can be calculated entirely based on prior knowledge. This prior knowledge can include experimentally measurable spectral characteristics of various benchmark real signals. Such spectral characteristics can, for example, be the fluorescence emission spectrum of a fluorophore or the illumination spectrum of a specific light source.

[0102] By minimizing, for example, by minimizing L n Norm to calculate the estimated value of the unknown mixture tensor M

[0103]

[0104] Here, n is an integer greater than zero.

[0105] Once the estimated value of the unknown mixture tensor M is calculated A preliminary estimate of the signal generated by the sources 122, 124, 126 can be calculated from the recorded signals 104, 106, 108 (i.e., y) as follows:

[0106]

[0107] in The initial estimated value of the reference real signal 128, 130, 132 with index d at the subset 118 with index p is the "demixed signal". For example, the concentration of fluorophores that produce a reference true signal with index d within a region of object 120 corresponding to subset 118 is indicated. Represents a preliminary estimate of the ground truth signal strength that is independent of the underlying color space.

[0108] In order to further improve the demixing signal An amount of crosstalk representing crosstalk between the signals 104, 106, 108 is calculated and removed from the unmixed signal with the same accuracy.

[0109] The crosstalk effect, or equivalently, the dependence of one unmixed signal on another unmixed signal, can be seen in Figure 3 Please explain.

[0110] Figure 3The crosstalk effect is schematically shown. Along the vertical axis, the intensity I of the signal 104 in the digital input image 100 is 104 Along the horizontal axis, the intensity I of the second signal 106 in the digital input image 100 106 Each measurement point 300 in the figure represents the intensity of the signal measured in a different subset 118 of the digital color input image 100 .

[0111] Reference numeral 302 indicates an intensity range that is present only in subset 118 of signal 104. Therefore, these intensities can be attributed to signal 104. Reference numeral 304 indicates an intensity range that is present only in subset 118 of signal 106 and can be attributed to signal 106. Intensity ranges 302 and 304 exhibit a spread that can be represented by statistical measures 306 and 308, respectively. One such measure can be the standard deviation.

[0112] In a subset of the digital color input image 100 where both signals 104 and 106 are present, for example Figure 1B In a subset 118 of region 110, the unmixed signal can exhibit a range of intensities 304. It can be seen that in the presence of signal 106, the diffusion metric 310 of signal 104 is greater than the metric 306 of signal 104 when no additional signal is present. The difference between metrics 306 and 310 is due to crosstalk, or spillover, from signal 106 onto signal 104. Similarly, the same is true for signal 106. In the presence of signal 104, the diffusion metric 312 of signal 106 is greater than the metric 306 of signal 106 when no additional signal is present. The difference between metrics 308 and 312 is due to crosstalk, or spillover, from signal 104 onto signal 106. These differences are captured by a crosstalk metric, which represents the dependence of one signal on another.

[0113] Crosstalk is not considered in spectral unmixing. Therefore, the unmixed signal still contain crosstalk components, especially if the signals 104, 106, 108 have spectral overlap in the subset 118, i.e., the unmixed signals Can represent one or more unmixed signals where d0≠d1.

[0114] For the first unmixed signal in the subset 118 with index p With the second unmixed signal The estimated value of the dependency between can be expressed as a (first) crosstalk quantity. With the first unmixed signal The estimated value of the dependence between can be expressed as another (second) crosstalk quantity.d There is a crosstalk amount C mn , where m = 1...N d , n=1...N d And m≠n, indicating the dependency of the mth signal on the nth signal.

[0115] At least one crosstalk quantity can be calculated using statistical methods, for example, by calculating the crosstalk of various non-identical unmixed signals. The correlation between pairs, especially the cross-correlation or covariance, is obtained.

[0116] In one example, the demixed signal A covariance tensor or matrix, or a Fisher information tensor or matrix, is calculated. In this case, the crosstalk amount can be derived or represented by an element, in particular a non-diagonal element, of the covariance matrix or the Fisher information matrix.

[0117] The amount of crosstalk can also be calculated using a Bayesian estimator.

[0118] In another more specific example, the crosstalk matrix may include or be composed of elements C mn These elements can be obtained by performing X on the covariance matrix 2 - Minimize the calculation.

[0119] In addition to other known methods for calculating the covariance, the elements of the covariance tensor are preferably calculated as follows:

[0120]

[0121] Where d1 and d2 indicate different unmixed signals. Since the estimated value of the mixing tensor M is calculated separately for each subset 118 and depends on the signals 104, 106, 108 in the corresponding subset 118, so the covariance tensor Cov differs from one subset to another, i.e. strictly speaking, The amount of crosstalk may be derived from or correspond to at least some of the off-diagonal elements of the covariance tensor.

[0122] To calculate the crosstalk amount C of the subset 118 with index p p , the covariance tensor can be weighted with a preferably user-controllable weight factor α, and its off-diagonal elements can be combined, for example, by addition:

[0123]

[0124] This amount of crosstalk may be subtracted, for example, from the unmixed signal of the subset 118 with index p to obtain an improved estimate of the reference true signal 128 , 130 , 132 in each subset 118

[0125]

[0126] If the above subtraction results in a negative value, then Set to zero.

[0127] Alternatively, another crosstalk quantity The crosstalk quantity C can be optionally normalized in the form of a confidence value

[0128] It can be deduced that:

[0129]

[0130] The confidence value represents a scalar, normalized metric, which is used to indicate the proportion of the crosstalk in the total strength of the demixed signal in each subset 118. The confidence value cannot be negative, which facilitates subsequent calculations.

[0131] Using the confidence value as the crosstalk amount, the ground truth can be calculated from the unmixed signal as follows

[0132] Improved estimate of the signal:

[0133]

[0134] In this formula, the crosstalk is multiplicatively removed from the unmixed signal. The unmixed signal is weighted by the confidence value. If the crosstalk represented in the off-diagonal elements of the crosstalk tensor increases, the estimated value will decrease.

[0135] Figure 2 A schematic diagram shows how a method 200 for enhancing a demixed signal 210 , 212 , 214 may be performed.

[0136] In a first step 202, a digital color input image 100 may be generated, for example, by at least one camera of the optical observation device 134. Alternatively, an already generated digital color input image 100 may also be retrieved directly from the camera or from a storage device.

[0137] At step 204, the subset 118 ( Figure 1B ) for spectral unmixing.

[0138] In step 206, the end member 208 may be retrieved, for example, from a storage device and input into the spectral unmixing process or step 204. As a result of the spectral unmixing, a plurality of unmixed signals 210, 212, ..., 214 are extracted from the subset 118. These unmixed signals 210, 212, 214 correspond to the above-mentioned For example, the unmixed signal 210 corresponds to the first unmixed signal The unmixed signal 212 corresponds to the second unmixed signal The unmixed signal 214 may correspond to a third, fourth, or generally “other” unmixed signal. Where d≥2.

[0139] In step 216, a first crosstalk measure 218 and any additional crosstalk measures 220 are calculated as described above. The number of crosstalk measures calculated in step 216 may depend on the number of unmixed signals extracted from the corresponding subset 118 in step 204. The calculation of at least one crosstalk measure 218 may depend on the estimated value of the mixing tensor 230 determined in step 204.

[0140] In step 222, the crosstalk amount 218 may be removed from the respective unmixed signals 210, 212, either subtractively or multiplicatively, to obtain improved signal estimates 224, 226, ie

[0141] In step 232, a weighting factor 234, ie, a, for calculating the amount of crosstalk is retrieved. For example, the weighting factor may be input by a user.

[0142] Post-processing of the signal estimates may be performed in step 228, such as displaying the signal estimates 224, 226 or combining them into a single digital output image.

[0143] Some embodiments relate to a microscope comprising a microscope comprising a microscope comprising a microscope comprising a microscope comprising a microscope Figure 3 Alternatively, the microscope may be a system that combines FIG1 to FIG1. Figure 3 One or more of the systems described herein are part of or connected to the system. Figure 4 A schematic diagram of a system 400 configured to perform the methods described herein is shown. System 400 includes a microscope 410 and a computer system 420. Microscope 410 is configured to capture images and is connected to computer system 420. Microscope 410 may include a data processing device 430, which may be an embedded processor of microscope 410 or system 400.

[0144] Computer system 420 is configured to perform at least a portion of the methods described herein. Computer system 420 can be configured to execute a machine learning algorithm. Computer system 420 and microscope 410 can be separate entities, or can be integrated together in a common housing. Computer system 420 can be part of a central processing system of microscope 410 and / or computer system 420 can be part of a subassembly of microscope 410 (e.g., a sensor, actuator, camera, or lighting unit, etc.).

[0145] The computer system 420 can be a local computer device (e.g., a personal computer, laptop, tablet computer, or mobile phone, etc.) having one or more processors and one or more storage devices; or it can be a distributed computer system (e.g., a cloud computing system having one or more processors and one or more storage devices distributed in various locations (e.g., at a local client and / or one or more remote server groups and / or data centers)). The computer system 420 can include any circuit or combination of circuits. In one embodiment, the computer system 420 can include one or more processors, which can be of any type. Here, "processor" can refer to any type of computing circuit, such as, but not limited to, a microprocessor of a microscope or a microscope component (e.g., a camera), a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA), or any other type of processor or processing circuit. Other types of circuitry that may be included in computer system 420 may include custom circuitry, application-specific integrated circuits (ASICs), and the like, such as one or more circuits (e.g., communications circuitry) used in wireless devices such as mobile phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. Computer system 420 may include one or more storage devices, which may include one or more memory elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard disks, and / or one or more drives for handling removable media such as compact discs (CDs), flash memory cards, digital video discs (DVDs), and the like. Computer system 420 may also include a display device, one or more speakers, and a keyboard and / or a controller, such as a mouse, trackball, touch screen, voice recognition device, or any other device that allows a system user to input information to and receive information from computer system 420.

[0146] Some or all of the steps of the method may be performed by (or using) hardware devices, such as processors, microprocessors, programmable computers or electronic circuits. In some embodiments, one or more of the most important steps of the method may be performed by such devices.

[0147] Depending on certain implementation requirements, embodiments of the present invention can be implemented in hardware or software. This implementation can be performed using a non-volatile storage medium, for example a digital storage medium, such as a floppy disk, DVD, Blu-ray disc, CD, ROM, PROM, EPROM, EEPROM, or flash memory, on which electronically readable control signals are stored, which can (or can be used to) work in conjunction with a programmable computer system to perform the corresponding method. Thus, the digital storage medium can be computer-readable.

[0148] Some embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.

[0149] Generally speaking, the embodiments of the present invention can be implemented as a computer program product having a program code, which is used to perform one of the methods described when the computer program product is run on a computer. For example, the program code can be stored on a machine-readable carrier.

[0150] Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.

[0151] In other words, an embodiment of the present invention therefore provides a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

[0152] Therefore, another embodiment of the present invention provides a storage medium (or data carrier, or computer-readable medium) having a computer program stored thereon, which, when executed by a processor, is configured to perform one of the methods described herein. The data carrier, digital storage medium, or recorded medium is typically tangible and / or non-transitory. Another embodiment of the present invention is an apparatus as described herein, comprising a processor and a storage medium.

[0153] Therefore, another embodiment of the present invention provides a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the sequence of signals may, for example, be configured to be transmitted via a data communication connection, such as via the Internet.

[0154] A further embodiment comprises a processing means, for example a computer or a programmable logic device, configured to or adapted to perform one of the methods described herein.

[0155] A further embodiment comprises a computer having installed thereon the computer program for performing one of the methods described herein.

[0156] According to another embodiment of the present invention, an apparatus or system is configured to transmit (e.g., electronically or optically) a computer program for performing one of the methods described herein to a recipient. For example, the recipient may be a computer, a mobile device, or a storage device. The apparatus or system may include, for example, a file server for transmitting the computer program to the recipient.

[0157] In some embodiments, a programmable logic device (e.g., a field programmable gate array) can be used to perform some or all of the functions of the methods described herein. In some embodiments, a field programmable gate array can cooperate with a microprocessor to perform one of the methods described herein. Generally, these methods are preferably performed by any hardware device.

[0158] Reference numerals

[0159] 100 digital color input images

[0160] 102 pixels

[0161] 104 First Signal

[0162] 106 Other signals / Second signal

[0163] 108 Other signals / third signal

[0164] 110 overlapping areas

[0165] 112 overlapping areas

[0166] 114 overlapping areas

[0167] 116 overlapping areas

[0168] 118 subsets

[0169] 120 Objects to be observed

[0170] 122 Source of the First (Realistic) Signal

[0171] 124 Source of the Second (Realistic) Signal

[0172] 126 The Source of the Third (Realistic) Signal

[0173] 128 reference real signals

[0174] 130 Benchmark Real Signal

[0175] 132 Benchmark Real Signal

[0176] 134 Optical Observation Device

[0177] 200 Methods

[0178] 202 Generate / Generate Digital Color Input Image

[0179] 204 Spectral Unmixing

[0180] 206 Retrieve Endmember

[0181] 208 Duan Yuan

[0182] 210 First unmixed signal / preliminary estimate of the first signal

[0183] 212 Second unmixed signal / preliminary estimate of the second signal

[0184] 214 Other unmixed signals / preliminary estimates of other signals

[0185] 216 Calculating Crosstalk

[0186] 218 (First) Crosstalk

[0187] 220 Other crosstalk

[0188] 222 Remove crosstalk amount

[0189] 224 Estimated value of the first signal

[0190] 226 Estimated values ​​of other signals

[0191] 228 Post-processing

[0192] 230 Mixed Tensor / Matrix

[0193] 232 Retrieval weight factor

[0194] 234 Weight Factor

[0195] 300 measuring points

[0196] 302 Intensity distribution when only the first signal exists

[0197] 302 Intensity distribution when only the second signal exists

[0198] 304 Intensity distribution when both signals are present

[0199] 306 Diffusion Metrics of Distribution 300

[0200] 308 Diffusion measure of distribution 302

[0201] 310 Diffusion measure of the first signal when both signals are present

[0202] 312 Diffusion measure of the second signal when both signals are present

[0203] 400 Optical observation device / system

[0204] 410 Microscope

[0205] 420 Data processing equipment / (general purpose) computers

[0206] 430 Embedded Systems

[0207] I Signal strength

[0208] λ wavelength

Claims

1. A computer-implemented method (200) for calculating an estimate (224) of a first signal (104) of a plurality of signals, the plurality of signals being contained in a digital color input image (100), each signal of the plurality of signals having a different reference true spectrum (302, 308), the digital color input image (100) comprising a plurality of pixels (102); in, The computer-implemented method (200) comprises the following steps: - extracting an appropriate subset (118) from the plurality of pixels (102); - extracting from said subset (118) by spectral unmixing (204): = a preliminary estimate of the first signal (104) as a first unmixed signal (210), and = a preliminary estimate of at least one other signal (106, 108) of the plurality of signals as at least one other unmixed signal (212, 214); - calculating (216) an estimate of the dependence of the first unmixed signal (210) on the at least one other signal (212, 214) from the subset (118) as an amount of crosstalk (218, 220) for the subset; and - removing (222) the amount of crosstalk (218, 220) from the first unmixed signal (210) to obtain the estimated value (224) of the first signal (104) in the subset (118).

2. The computer-implemented method (200) of claim 1, in, The step of extracting a preliminary estimate of at least one other signal (106, 108) of the plurality of signals from the subset (118) as at least one other unmixed signal (212, 214) by spectral unmixing (204) comprises the following steps: - extracting a preliminary estimate of a second signal (106) of the plurality of signals from the subset (118) as a second unmixed signal (212) by spectral unmixing (204); and The computer-implemented method (200) further comprises the following steps: - calculating an estimate of the dependence of the second unmixed signal (212) on the first unmixed signal (210) as a second amount of crosstalk (220) in the subset (118); and - removing the second amount of crosstalk (220) from the second unmixed signal (212) to obtain an estimate (226) of the second signal (106) in the subset (118).

3. The computer-implemented method (200) according to claim 1 or 2, in, The step of extracting by spectral unmixing (204) comprises one of the following: - Extraction by linear spectral unmixing; -Extraction via non-negative tensor decomposition.

4. The computer-implemented method (200) according to any one of claims 1 to 3, in, The step of calculating (216) the amount of crosstalk (218, 220) comprises: - calculating the probability that the first unmixed signal (210) corresponds to the first signal (104).

5. The computer-implemented method (200) according to any one of claims 1 to 4, in, The step of calculating the amount of crosstalk (218, 220) includes calculating a correlation.

6. The computer-implemented method (200) according to any one of claims 1 to 5, in, The step of calculating (216) the amount of crosstalk (218, 220) includes calculating a crosstalk tensor, the amount of crosstalk (218, 220) of the crosstalk tensor being an element.

7. The computer-implemented method (200) of claim 6, in, The step of calculating (216) the crosstalk tensor comprises calculating at least one of: - Calculate the covariance tensor; - Compute the Fisher Information Tensor.

8. The computer-implemented method (200) according to any one of claims 1 to 7, in, The step of removing (222) the crosstalk amount (218, 220) from the unmixed signal (210, 212) comprises one of the following: - subtracting the crosstalk amount (218, 220) from the unmixed signal (210, 212); and - multiplying the unmixed signal (210, 212) by the crosstalk amount (218, 220).

9. The computer-implemented method (200) according to any one of claims 1 to 8, in, The subset (118) corresponds to a single pixel (102) of the plurality of pixels of the digital color input image (100).

10. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 9.

11. A computer-readable medium having stored thereon the computer program according to claim 10.

12. A method of using a medical optical observation device, such as a microscope or an endoscope, comprising the steps of: - recording at least one color image and generating a color input image based on the at least one color image; - Performing a computer-implemented method according to any one of claims 1 to 9.

13. A data processing apparatus (420, 430) for calculating an estimated value (224) of a first signal (104) of a plurality of signals, the plurality of signals being contained in a digital color input image (100), each signal of the plurality of signals having a different reference true spectrum (302, 308), in, The data processing device (420, 430) is configured to: - retrieving a suitable subset (118) of said plurality of pixels (102) of said digital color input image (100); - extracting from said subset (118) by spectral unmixing (204): = a preliminary estimate of the first signal (104) as a first unmixed signal (210), and = a preliminary estimate of at least one other signal (106, 108) of the plurality of signals as at least one other unmixed signal (212, 214); - calculating (216) the first unmixed signal (210) versus the at least one other signal (212, 214), and using the estimated value of the dependency as the crosstalk amount (218, 220) of the subset (118); and - removing (222) said crosstalk amount (218, 220) from said first unmixed signal (210), to obtain the estimated value (224) of the first signal (104) in the subset (118).

14. An optical observation device (400, 410), such as a microscope or an endoscope, configured to record a digital color input image (100), the medical optical observation device (400, 410) further comprising: The data processing device (420, 430) according to claim 13.

15. The optical observation device (400, 410) according to claim 14, in, The data processing device (420) includes or consists of the embedded system (430) of the optical observation device (400, 410).

Images