Apparatus for layer thickness measurement and method thereof

Abstract

A first aspect of the present disclosure relates to an imaging device for determining a thickness of a layer included in an image, configured to: acquire an image of a sample having one or more layers; obtaining first estimation information indicating a position of a first edge of the layer from the user interface; obtaining, from the user interface, second estimation information indicating a position of a second edge of the layer; determining a first edge of the layer based on the first estimation information; determining a second edge of the layer based on the second estimation information; and determining a distance between the first edge and the second edge.

Description

Technical Field

[0001] This disclosure relates to an apparatus for measuring the thickness of a sample layer and a method for operating the apparatus for measuring thickness. Background Technology

[0002] In imaging, such as the microscopic analysis of samples, the thickness of one or more layers within a sample, or the thickness of one or more layers within a sample, can be an important parameter. Current market products require users to manually identify points for measuring distances. These operations are time-consuming and complex, and may require significant manual input and user knowledge. Automated measurement methods also exist, but these methods often measure distances that do not represent the desired layer thickness because they identify incorrect measurement points. Therefore, improvements in layer thickness measurement are desirable. Summary of the Invention

[0003] The purpose of this disclosure is to improve slice thickness measurement in imaging systems.

[0004] This objective is achieved through the disclosed embodiments, which are particularly defined by the subject matter of the independent claims. The dependent claims provide information for other embodiments. Various aspects and embodiments of these aspects are also disclosed in the following summary and description, providing additional features and advantages.

[0005] The first aspect of this disclosure relates to an imaging apparatus for determining the thickness of layers included in an image, configured to:

[0006] - Obtain images of samples with one or more layers;

[0007] - Obtain first estimated information about the position of the first edge of the indicator layer from the user interface;

[0008] - Obtain second estimated information about the location of the second edge of the indicator layer from the user interface;

[0009] - Determine the first edge of the layer based on the first estimation information;

[0010] - Determine the second edge of the layer based on the second estimation information; and

[0011] - Determine the distance between the first edge and the second edge.

[0012] Acquiring an image may include receiving and / or obtaining image information associated with that image. Alternatively or additionally, acquiring an image may include determining that information based on other received / obtained information.

[0013] Imaging devices can be configured to examine objects that are difficult to see with the naked eye. Examples of imaging devices can be microscopes, such as wide-field or bright-field microscopes, transmitted light microscopes, reflected light microscopes, phase-contrast microscopes, etc. Microscopes also include macroscopes and stereoscopes. Another example of an imaging device is an endoscope, and yet another example is an exoscope.

[0014] The sample can be any type of sample that can be examined by the imaging device and can be organic or inorganic. Alternatively or additionally, the sample may include any other elements or molecules. For example, the sample may be tissue or rock or made of another material, such as plastic or metal or compounds thereof. The sample may be opaque, transparent, or in any state in between.

[0015] Because of one or more layers visible in an image captured by an imaging device, the image displays corresponding edge lines, where two adjacent layers can be separated from each other by one of the edge lines.

[0016] The user interface can be a component of the imaging device. Optionally, the user interface can be detached from and connected to the imaging device for data transmission. In particular, the user interface may include a display configured to show the user an image acquired by the imaging device. The user interface may have an input module through which the user can input information, such as first and second estimation information. The user interface may, for example, include one or more of the following: a touch display, a pointing device (e.g., a computer mouse or trackball or touchpad, a computer keyboard, a stylus, etc.), an audio detection device, and / or a gesture detection device.

[0017] To determine the first and second edges of one of the layers displayed in an image, the imaging device is configured to receive or acquire user input via a user interface, representing first and second estimation information, respectively. For example, the user marks shapes that follow and / or correspond to the edges, or at least one or more discrete points located on the respective edges. Alternatively or additionally, the user provides information independent of a single pattern in the image, i.e., based solely on user experience and / or on multiple imaging artifacts. The edges of the layers are determined based on the acquired first and second estimation information via an algorithm (which may be a software module), as a component of the imaging device.

[0018] The determination of the first and / or second edges can be based on interpolation and extrapolation of user information. Alternatively, the determination of the first and / or second edges can be based on a model used to calculate edges based on user information. Alternatively, the determination of the first and / or second edges can be based on machine learning algorithms, such as neural networks, fed with user information about the first and / or second edges. The device can operate on a single image or on a real-time image stream. In the latter case, the device can be configured to acquire first and second estimation information for at least one image in the image stream and then determine the first and second edges throughout the entire image stream.

[0019] The algorithm may specifically include an edge recognition process aided by information from user input. These methods enable edge detection even when at least some edge information is below the noise level of the image.

[0020] The imaging device is further configured to determine the distance between the first edge and the second edge, specifically by means of the previously mentioned algorithm and / or another algorithm (which may be another software module) that is a component of the imaging device. In other words, the imaging device is configured to measure the thickness of the layer on the image defined by the image edges identified by the user.

[0021] Imaging devices are advantageous in that they are user-friendly and / or can reduce human error in determining layer edges and thus layer thickness. This is because very little user intervention is required to provide the necessary data to the imaging device, enabling the device (especially one or more of its algorithms) to perform actions / operations to determine layer edges and distances between these layers. Therefore, layer edges and subsequent layer thicknesses can be detected based on complementary human and automated knowledge.

[0022] Optionally, the imaging device includes components configured to provide a measured distance (i.e., layer thickness) to a user, such as a visual and / or audio display, a printer, etc. These components may be part of the user interface.

[0023] The first aspect of the embodiment relates to an imaging apparatus for determining the thickness of layers in an image, wherein the first and / or second estimation information can include one or more of the following:

[0024] - One or more points,

[0025] - One or more lines, or,

[0026] - One or more regions.

[0027] The imaging device can be configured to acquire corresponding estimation information from a user in the form of input data characterizing one or more points, one or more lines, and / or one or more regions. The imaging device can be further or optionally configured to determine (i.e., interpolate and / or extrapolate) first and / or second layer edges based on the input points, lines, and / or regions, for example, by the aforementioned algorithm or another algorithm (which may be a software module) that is a component of the imaging device. The user can input points, lines, and / or regions in a particularly simple manner using a user interface, such as through touch input. These points, lines, and / or regions are then used as first and second estimation information, or, based on these points, lines, and / or regions, to generate first and second estimation information.

[0028] Although layer edges are determined based on user-inputted information (points, lines, and / or regions), the user does not need to painstakingly identify the complete edges and provide their curvature to the system. Instead, the system (i.e., the imaging device) can automatically identify layer edges starting from the corresponding points and / or regions, indicating that the lines displayed in the image (i.e., the image on which the user has set points and / or regions) are indeed layer edge lines, not any other stripes (non-edge lines). Layer edge lines represent layer edges in the image, while any other stripes can represent any other visible edge-like structure in the image besides layer edges. Although layer edges are determined based on lines input by the user, the exact curvature of the layer-separating edge lines can be indicated, thus facilitating layer edge detection or determination.

[0029] The first aspect of the embodiment relates to an imaging device for determining the thickness of layers in an image, wherein the estimation information can include areas provided by a brush tool on a user interface.

[0030] It should be understood that the brush tool is a virtual tool, for example, appearing on the user interface and / or the display of another user interface, which is part of or connected to the imaging device, such as in the form of a mouse pointer symbol, touch input, and / or similar means. This indicates to the user that a brush marking mode for marking layer edges (particularly for input areas) is activated. The purpose is for the user to use the virtual brush tool to trace a portion or the entire layer edge. In this process, the user marks at least one area containing the corresponding edge. This means that the brush tool can be enabled to provide one or more areas according to the embodiments described above. With the brush tool, first and / or second estimation information can be provided along edges that vary greatly and / or have particularly many curves. Nevertheless, the curvature of the layer edge can be determined with particular accuracy.

[0031] The first aspect of the embodiment relates to an imaging device for determining the thickness of layers in an image, configured as follows:

[0032] - Adjust the parameters of the brush tool, especially one or more of the following:

[0033] -- Width of the brush stroke;

[0034] -- The color of the brushstrokes; and

[0035] -- Opacity of the brush stroke.

[0036] Embodiments having these features can have two general forms.

[0037] In a first general implementation, one or more brush stroke parameters are adjusted by the device (or a function of the device) so that information about edge detection can be displayed to the user. For example, the user provides information about edges using a brush tool, and the brush stroke color varies depending on the edge recognition result.

[0038] - Green indicates a confirmed edge detection result;

[0039] - Orange is used for edge recognition results that are less certain; and

[0040] - Red indicates edges that were not detected.

[0041] By parameterizing the brush stroke based on the results, the user can be informed of edge detection results and locations where they might want more information in different ways. In another implementation, the device is configured to adjust the width of the (user-provided) brush stroke after edge detection is performed. This can be done by precisely indicating the location where an edge was found, as well as the location where multiple edge candidates were found. For example, the width of the brush stroke can be reduced to the detected edge (if an edge was found) and / or the width of the brush stroke can be widened or reduced to the area where multiple edge candidates were found. In particular, all alternatives can be applied to a single user-provided brush stroke, thereby indicating to the user all possible result types.

[0042] In the second general implementation, one or more brush stroke parameters are adjusted by the user, and the adjustment information is received through a user interface. Therefore, the user can provide the device with simplified information about edges. For example, the user can exclude other edges or edge-like structures (see above: any other visible edge-like structures in the image besides layer edges representing samples) by adjusting the size of the brush tool (specifically its width).

[0043] Brush strokes can also provide information about the probability of an edge existing within the area indicated by the brush tool. For example, the center of a brush stroke may indicate a high probability that the edge is located within the indicated area, while the outer areas of the brush stroke may indicate a low probability. Specifically, the probability across the width of a virtual brush stroke can be uniformly or normally distributed (e.g., the highest probability is at the center of the brush stroke). In this case, different colors of the brush stroke can also provide different probabilities for the user.

[0044] This allows for efficient and rapid indication of edges within an image, whether as input (from user to device) or as output (from device to user).

[0045] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0046] - Based on the first and / or second estimation information, the margin is determined according to one or more of the following:

[0047] -- The intensity difference within the corresponding estimated information;

[0048] -- Spectral differences within the corresponding estimated information;

[0049] -- Color difference within the corresponding estimated information;

[0050] -- Applied to the corresponding estimation information, especially to the trained machine learning algorithms applied to the first and second estimation information.

[0051] Therefore, the imaging device is configured to analyze user-input data, i.e., corresponding estimation information, and provide the result of the analysis, i.e., whether the first and / or second estimation information contains image data and / or defines and / or specifies image data indicating the presence of at least a segment of edge or layer edge within the corresponding estimation information. Thus, the imaging device may have an imaging processor configured to perform the analysis, i.e., determine the intensity difference, spectral difference, and / or color difference between two adjacent layers in the image, separated by the layer edges indicated by the user through the provision of the first and second estimation information.

[0052] Alternatively or concurrently, the imaging device may incorporate a machine learning algorithm that can identify two adjacent layers and their resulting common layer edges in the collected image data. The machine learning algorithm can be trained to recognize repeating patterns within provided brush strokes or areas. Specifically, the machine learning algorithm can be designed to perform the edge recognition process.

[0053] Therefore, layer edges can be automatically detected within the estimation information provided by the user.

[0054] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0055] - If the determination of the first and / or second edge generates multiple first or second edge candidates, the first edge is automatically determined based on the first edge candidate, and / or the second edge is automatically determined based on the second edge candidate.

[0056] For example, the imaging device is configured to automatically select one edge from a first set of edge candidates as a first layer edge. Optionally or additionally, the imaging device is configured to automatically select one edge from a second set of edge candidates as a second layer edge. The imaging device may, optionally or additionally, be configured to automatically select a first edge by connecting two or more first edge candidates, and optionally or additionally, to automatically select a second edge by connecting two or more second edge candidates. Thus, one or more edges can be selected without user intervention. Additionally or optionally, the device may select among multiple edge candidates such that only a few candidates are connected as layer edges and / or such that only a single candidate is selected as a layer edge. The above selection can be implemented by other software-based modules involving performing candidate reduction (i.e., selection). The modules may include machine learning algorithms trained to select one or more edges from multiple edge candidates.

[0057] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0058] - Define the first and / or second edge as one or more of the following:

[0059] -- Edge candidates with the maximum length;

[0060] -- Along the edge candidates that determine the maximum intensity change; and

[0061] -- Candidates along the edge with the largest sum of their intensity changes.

[0062] The imaging device can be configured to automatically select and classify a layer edge from the identified edge candidates based on their length. In this case, the imaging device may indicate the longest edge candidate (i.e., the edge candidate with the largest length) as the actual layer edge. Optionally or additionally, the imaging device may be configured to automatically select and classify a layer edge from the identified edge candidates based on the intensity change between layers separated by the edge candidates. Thus, the imaging device may indicate one of the edge candidates representing the strongest intensity transition between two regions (i.e., two layers) of the sample as the actual layer edge. The imaging device may also be configured to optionally or additionally select and classify a layer edge from the identified edge candidates based on the sum of the intensity changes along the respective edge candidate. This means that the imaging device identifies the edge candidate with the largest sum of its intensity changes. The identified edge candidate will be classified by the imaging device as an actual edge layer. Thus, one or more edges can be detected particularly accurately and reasonably. Furthermore, the identification can be used as a plausibility check to verify that the selected edge candidate is indeed a layer edge, and not merely a sample structure that might be confused with a layer edge.

[0063] The first aspect of the embodiment relates to an imaging device for determining the thickness of layers in an image, configured as follows:

[0064] - If the determination of the first and / or second edge yields multiple first or second edge candidates:

[0065] -- Display edge candidates in the user interface;

[0066] -- Obtain user information related to one or more edge candidates; and

[0067] -- Determine the first edge and / or the second edge based on the acquired user information.

[0068] Therefore, user-based, experience-based knowledge is used advantageously, particularly to avoid ambiguity in identifying actual layer edges, and (if ambiguity exists) to eliminate such ambiguity in subsequent determination processes. Specifically, the acquired user information can be used to train machine learning algorithms to further improve their ability to automatically distinguish between non-edge structures displayed in an image and actual layer edges.

[0069] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0070] - Divide the first edge and / or the second edge into multiple segments; and

[0071] - Determine the distance of each layer for each segment.

[0072] Segments can have the same size, i.e., length. Optionally, segments can have different sizes / lengths. According to this embodiment, it is also conceivable to divide the layer edge into some segments of equal size and some segments of unequal size. Typically, the distance between different distance estimates can be provided in advance by the user and / or adjusted online by the user, for example, using a mouse.

[0073] The imaging device is configured to measure the distance between each identified segment, thus providing distance estimates for different parts of the layer between the segment edge layers.

[0074] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0075] - Identify multiple distance candidates at different locations between the first and second edges; and

[0076] - Determine the distance as the candidate distance with the shortest distance.

[0077] The imaging device can be configured to measure the distance between layer edges at multiple points along the edge and identify the shortest candidate distance among the measured distances. The imaging device can be further configured to provide the shortest distance to a user, for example, through a user interface, display, etc. In this way, the global minimum distance between edges, i.e., the layer thickness, can be determined in a particularly efficient manner.

[0078] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0079] - Identify multiple additional distance candidates near the previously determined distance; and

[0080] - The second distance is identified as another distance candidate with the shortest distance.

[0081] To improve the results collected by determining multiple distance candidates at different locations between the first edge and the second edge and identifying the shortest distance candidate as the global minimum of the layer thickness, according to this embodiment, the imaging device can be configured to measure additional distances near the distance candidate representing the global minimum of the layer thickness.

[0082] For example, one or more additional distance candidates can be determined above the previously determined distance, and optionally or additionally, one or more additional distance candidates can be determined below the previously determined distance. The imaging device can then be further configured to identify the shortest distance candidate among the additional distance candidates and classify it as the additional shortest distance between layer edges, i.e., as an additional global minimum of the layer thickness. Specifically, both the step of determining the additional distance candidates and the step of determining the shortest distance candidate among the additional distance candidates can be repeated until no shorter distance candidate between layer edges is found. As a result of this embodiment, the overall global minimum of the layer thickness can be determined with particularly high precision.

[0083] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0084] - Identify the protrusions at the first edge and / or the second edge; and

[0085] - Determine the distance based on one or two protruding points.

[0086] This is advantageous because the shortest distance between the edges of the first and second layers is more likely to occur at the salient points. This may even be the global minimum of the layer thickness. To determine the corresponding salient points, the imaging device can be configured to identify the point where the corresponding layer edge extends furthest in the direction toward the opposite layer edge. Alternatively, the salient points of an edge can be determined by identifying the edge and subsequently analyzing the spectrum along the edge line. High frequencies can indicate salient points from which distance measurements can be taken.

[0087] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0088] - Determine the centerline of the layer based on the determined first and second edges; and

[0089] - Determine one or more distances for the layer, wherein one distance is determined as a line perpendicular to the center line between two edges.

[0090] The centerline can be determined at the midpoint between the edges of two layers. For example, it can be a piecewise straight line or a non-straight line connecting all the center points between the edges of two layers.

[0091] To determine the distance, a straight line can be drawn that intersects the centerline perpendicularly and touches both edges. This line is bisected by the centerline. This connecting line comprises two bisecting lines, which are arranged linearly, and each bisecting line can be placed between the edge and the centerline. The length of this connecting line (i.e., the sum of the lengths of the two corresponding bisecting lines) is the distance between the two edges.

[0092] There may be as many distances between these edges as there are different connecting lines that can be determined. This method may be particularly effective for determining the shortest distance between edges; it simply requires selecting or outputting the determined shortest distance from all the acquired distances.

[0093] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0094] - Determine the convex side of the first and / or second edge and / or the convex side of a portion of the first and / or second edge; and

[0095] - Determine the distance between opposite sides of the corresponding edge.

[0096] This embodiment can be used to measure curvature or distance within a single layer edge in the first and / or second layer edges, for example, to examine protrusions in layers penetrating adjacent layers. The corresponding protruding layer edges are, for example, substantially U, V, or Omega shapes, or parabolic shapes, and surround the protruding layer region. According to this embodiment, the imaging device is configured to determine a first protrusion point and at least one second protrusion point on the corresponding layer edge, wherein the first protrusion point is located on an ascending branch of the layer edge, and the second protrusion point is located on a descending branch of the same layer edge. The imaging device is further configured to measure the linear distance between the first and second protrusion points, thereby determining the distance between opposite sides or branches of the protruding edge layer or edge layer portion. Optionally, the process described in the above embodiment can be used to render the centerline of the protrusion (i.e., the convex side of the corresponding layer edge) and determine the extension of the protruding layer region relative to the centerline. Another option is to set a boundary line for the protruding layer region, wherein the distance between opposite sides or branches of the protruding edge layer is parallel to this boundary line.

[0097] The first aspect of the embodiment relates to an imaging device for determining the thickness of an inner layer of an image, configured as follows:

[0098] - Determine multiple thicknesses of the layer; and

[0099] - Determine one or more of the following:

[0100] -- Maximum thickness of the layer;

[0101] -- Minimum thickness of the layer;

[0102] -- The average thickness of the layer; and

[0103] -- Parameters indicating changes in the thickness of the indicative layer.

[0104] This provides the user with a comprehensive overview of the layer's size and / or shape. Parameters characterizing thickness variations can, for example, indicate whether the layer's thickness increases or decreases along a specified or specifyable reference direction of the layer.

[0105] A second aspect of this disclosure relates to a method for determining the thickness of an inner layer of an image, comprising the following steps:

[0106] - Obtain images of samples with one or more layers;

[0107] - Obtain first estimated information about the position of the first edge of the indicator layer from the user interface;

[0108] - Obtain second estimated information about the location of the second edge of the indicator layer from the user interface;

[0109] - Determine the first edge of the layer based on the first estimation information;

[0110] - Determine the second edge of the layer based on the second estimation information; and

[0111] - Determine the distance between the first edge and the second edge.

[0112] The above method can be implemented by a computer. Its steps can be performed by a computer system. Using the above method allows for edge detection based on complementary human and automated knowledge. In particular, the method can pause at certain points to wait for and / or receive user input.

[0113] A third aspect of this disclosure relates to a computer program having program code that, when run on a processor, performs the methods described in accordance with the above aspects. Attached Figure Description

[0114] Other advantages and features arise from the following embodiments, some of which are illustrated in the accompanying drawings. The drawings are not always to scale. Dimensions of various features may be enlarged or reduced, particularly for clarity of description. Therefore, the drawings are at least partially schematic.

[0115] Figure 1 Distance measurement according to an embodiment of this disclosure is shown.

[0116] Figure 2 Distance measurement according to an embodiment of this disclosure is shown.

[0117] Figure 3 Distance measurement according to an embodiment of this disclosure is shown.

[0118] Figure 4 Distance measurement according to an embodiment of this disclosure is shown.

[0119] Figure 5 A microscope system for use in embodiments of this disclosure is shown.

[0120] Although some aspects have been described in the context of an apparatus (or system) in this disclosure, the description of these aspects also represents a description of the corresponding method, wherein a block or device corresponds to a method step or a feature of a method step.

[0121] Similarly, aspects described in the context of method steps also represent descriptions of corresponding blocks, items, or features of corresponding devices or systems, which may in particular be distributed in different locations and configured to exchange information between the different locations using their respective means of communication.

[0122] Generally, the disclosure of the described method also applies to the corresponding device (or apparatus) performing the method or to a corresponding system comprising one or more devices, and vice versa. For example, if specific method steps are described, the corresponding device may include features for performing the described method steps, even if such features are not explicitly described or represented in the drawings. On the other hand, for example, if a specific device is described based on functional units, the corresponding method may include one or more steps to perform the described function, even if such steps are not explicitly described or represented in the drawings. Similarly, a system having corresponding device features or features for performing specific method steps may be provided. Unless explicitly stated otherwise, features of the various exemplary aspects and embodiments described above or below may be combined.

[0123] The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items and may be abbreviated as “ / ”. Expressions such as “for example” or “in particular” indicate optional or alternative features that may be combined with all other (mandatory, optional, or alternative) features of aspects or embodiments of this disclosure, unless otherwise expressly stated.

[0124] In the following description, reference is made to the accompanying drawings, which form part of this disclosure, illustrating specific aspects of which the disclosure may be understood. The same reference numerals denote the same or at least functionally or structurally similar features. Detailed Implementation

[0125] exist Figure 1 , Figure 2 and Figure 3 Images 100, 200, and 300 depict a sample 110 of the object to be examined, which has multiple layers 112, 116, 120, 130, and 134. Images 100, 200, and 300 were captured by an imaging device, specifically through a microscope 510 (see image 100). Figure 5 (Photographed)

[0126] Image 100 can be a still image (such as a photograph) or a moving playback of a video stream. In this example, five layers are visible in image 100: a first left layer 112, a second left layer 116, a center layer 120, a first right layer 130, and a second right layer 134. Thus, the first left layer 112 and the second left layer 116 are separated from each other by edge 114, the second left layer 116 and the center layer 120 are separated from each other by edge 122, the center layer 120 and the first right layer 130 are separated from each other by edge 124, and the first right layer 130 and the second right layer 134 are separated from each other by edge 132. In image 100, layer edges 114, 122, 124, and 132 are represented by corresponding layer edge lines. Furthermore, non-edge lines or stripes are indicated in image 100 by reference symbols 118 and 136. The stripes 118 and 136 visible in image 100 are generated by the edge-like structure of the object being inspected.

[0127] Imaging apparatus or microscope 510 is configured to perform a method for determining the thickness of one or more layers 112, 116, 120, 130, 134 visible in image 100. Hereinafter, the method will be described exemplarily with primary reference to the central layer 120. It should be understood that the method is equally applicable to one or both of layers 116, 130, since layers 116, 130 are respectively inserted between edges 114, 122 and 124, 132.

[0128] Therefore, images 100, 200, and 300 of sample 110, comprising layers 112, 116, 120, 130, and 134, are acquired using an imaging device or microscope 510. Additionally, first and second estimation information are obtained from a user interface. The first estimation information indicates the position of a first edge of one of the layers 112, 116, 120, 130, and 134, and the second estimation information indicates the position of a second edge of the same layer among layers 112, 116, 120, 130, and 134. For example, if a user wants information about the central layer 120, they intend to provide first and second estimation information about the central layer 120 (i.e., about the positions of layer edges 122 and 124), since the central layer 120 is located between edges 122 and 124. Furthermore, the method includes the steps of determining the first edge (here, edge 122) of the central layer 120 based on the first estimation information and determining the second edge 124 of the central layer 120 based on the second estimation information. In this example, the imaging device determines the position and curvature of edges 122 and 124 based on first and second estimation information provided by the user, respectively, characterizing edges 122 and 124. The method also includes the step of determining the distance between the first edge (here, edge 122) and the second edge (here, edge 124).

[0129] like Figure 1As can be seen in the image, distance measurements according to embodiments of the present disclosure are shown, and the first and / or second estimation information may include one or more points. The user provides points 140, 142, and 144, each located on a first edge 122 of the central layer 120. Additionally, the user provides the locations of points 150, 152, and 154, each located on a second edge 124 of the central layer 120. Thus, the system, i.e., the imaging device or microscope 510, is aware of at least some segments of edges 122 and 124. To determine one or more distances between edges 122 and 124, corresponding distances 160, 162, and 164 between edge points 140 and 150, 142 and 152, and 144 and 154 are measured. Distances 160, 162, and 164 are visualized in image 100 by connecting lines to the corresponding points. In one embodiment, a protruding point is identified from points 140, 142, 144 and / or from points 150, 152, 154, and a distance is determined based on the one or more protruding points. Alternatively or additionally, the first and / or second estimation information may include one or more lines, such as lines created by the user by tracing the corresponding edges 122, 124. One of these lines may, for example, extend between points 140 and 142 or between points 152 and 154.

[0130] In one embodiment, the first and / or second estimation information for determining edges 122 and / or 124 is determined based on intensity differences, spectral differences, and / or color differences between layers 116, 120 and / or 120, 130, which are adjacent to each other in image 100 and separated by edges 122 and 124, respectively. Alternatively or additionally, trained or trainable machine learning algorithms may be applied to the first and second estimation information.

[0131] According to one embodiment, multiple thicknesses of layer 120 are determined, and a maximum thickness, a minimum thickness, and / or an average thickness of layer 120 are determined. Optionally or additionally, a parameter is determined that indicates a variation in the thickness of layer 120. The parameter characterizing the variation in thickness may, for example, indicate whether the thickness of layer 120 increases or decreases along a specified or specifyable reference direction of layer 120 (e.g., along its longitudinal extension direction).

[0132] Through one or more embodiments described herein, multiple distance candidates at different locations between the first edge 122 and the second edge 124 can be determined. The distance candidate with the shortest distance is then determined as the distance between layer edges 122 and 124. Figure 1 In this context, the shortest distance candidate might first be, for example, a distance of 162. Figure 1As can be further seen, according to one embodiment, a plurality of additional distance candidates were determined, each of which was located near the previously determined distance 162. Subsequently, a second distance was determined as another distance candidate having the shortest distance. (See reference...) Figure 1 The shortest distance candidate among the other distance candidates results in a distance of 162, which is the second distance between edges 122 and 124.

[0133] Figure 2 Distance measurement according to an embodiment of this disclosure is illustrated. Figure 2 As can be seen, the first and / or second estimation information may include one or more regions, which are exemplarily depicted here in image 200 as regions 210, 212, 214, 216, 220, 222, 224, and 226. Figure 2 In this context, regions 210, 212, 214, 216 and 220, 222, 224, 226 each contain segments or sections of edges 122 and 124, respectively. To determine one or more distances between edges 122 and 124, the corresponding distances 230, 232, 234, and 236 between regions 210 and 220, 212 and 222, 214 and 224, and 216 and 226 are measured. For example, the distances 230, 232, 234, and 236 between two corresponding regions can be measured between the midpoints of two corresponding regions 210 and 220, 212 and 222, 214 and 224, and 216 and 226, respectively.

[0134] According to one embodiment, the method is applicable when a user can use a brush tool to provide one or more regions from regions 210, 212, 214, 216, 220, 222, 224, and 226 as first or second estimation information. Therefore, the user uses the brush tool, i.e., a virtual data input tool provided on the user interface, at least roughly along the edges he / she wants to mark. Because the brush tool is wider than the lines representing edges, the user can easily cover the corresponding edges with brush strokes. According to one embodiment, the width of the brush tool can be adapted to the requirements of image 200, such as its scale and / or magnification factor, to avoid covering non-edge elements during the application of the brush strokes.

[0135] According to one embodiment, the first edge 122 and / or the second edge 124 are divided into a plurality of segments of the same or different sizes. To determine one or two or more distances between edges 122 and 124, the corresponding distance between a segment of the first edge 122 and a segment of the second edge 124 is measured.

[0136] Figure 3Distance measurement according to an embodiment of this disclosure is illustrated. As can be seen in image 300, a center line 310 is defined, precisely positioned midway between the two layer edges 122 and 124 along its curvature. To determine one or more distances between edges 122 and 124, connecting lines are defined that intersect the center line 310 perpendicularly and contact the two edges 122 and 124. Each connecting line is bisected by the center line 310. This means that each connecting line comprises two linearly arranged bisecting lines. Simultaneously, each connecting line represents a corresponding distance 320, 322, 324, 326, 328 between edges 122 and 124.

[0137] Figure 4 A distance measurement according to an embodiment of the present disclosure is shown, wherein a protruding portion of the first edge 122 is examined. Figure 4 Image 400 shown, for example, is a magnified image of sample 110, so only layers 116, 120 and the edge 122 therebetween are visible. As can be seen in image 400, the protruding portion of the first edge 122 includes an ascending branch 402 and a descending branch 404. This protruding portion surrounds the protruding layer region 420 protruding from layer 120, thus penetrating layer 116. In this embodiment, first protrusion points 410, 412 and second protrusion points 414, 416 are identified. The first protrusion points 410, 412 are located on the ascending branch 402, while the second protrusion points 414, 416 are located on the descending branch 404 of edge 122. The distance 430 between the first protrusion points 412 and 414 and the distance 432 between the second protrusion points 410 and 416 can be measured to examine the protruding layer region 420. In other words, according to this embodiment, the corresponding distances between opposite sides of edge 122 or between branches 402, 404 are determined. Alternatively or alternatively, the centerline 422 of the protruding layer can be determined in a manner similar to the previous combination. Figure 3 Use it as described. Further, as... Figure 4 As shown, the boundary line 434 of the protruding layer region 420 can also be determined, wherein the protruding layer region 420 is surrounded by the protruding portion of the first edge 122 and the boundary line 434. In this case, the distances 430 and 432 can be aligned parallel to the boundary line 434.

[0138] If the determination of edges 122 and 124 generates multiple first and / or second edge candidates, one embodiment provides the automatic determination of one or both of edges 122 and 124 based on the corresponding first or second edge candidates. Optionally or additionally, a first edge 122 can be automatically determined by connecting two or more first edge candidates, and optionally or additionally, a second edge 124 can be automatically determined by connecting two or more second edge candidates.

[0139] Furthermore, if the determination of reference edges 122 and 124 generates multiple first and / or second edge candidates, then in one embodiment, the first edge 122 and / or the second edge 124 can be determined as the edge candidate with the maximum length of the corresponding edge candidate, the edge candidate with the maximum intensity change along which the corresponding edge candidate is determined, and / or the edge candidate with the maximum sum of the intensity changes along which the corresponding edge candidate is determined.

[0140] Furthermore, if the determination of edges 122 and 124 generates multiple first and / or second edge candidates, in one embodiment, the edge candidates are displayed via a user interface. Subsequently, user information associated with one or more edge candidates is acquired, and the first edge 122 and / or the second edge 124 is determined based on the acquired user information.

[0141] Some embodiments relate to a microscope, including a combination of Figures 1 to 3 One or more of the systems described herein. Optionally, the microscope may be combined with... Figures 1 to 4 It is a part of or connected to one or more of the systems described herein.

[0142] Figure 5 A schematic diagram of a system 500 configured to perform the methods described herein is shown. System 500 includes a microscope 510 and a computer system 520. The microscope 510 is configured to capture images and is connected to the computer system 520. The computer system 520 is configured to perform at least a portion of the methods described herein. The computer system 520 may be configured to execute machine learning algorithms. The computer system 520 and the microscope 510 may be separate entities, but may also be integrated into a common housing. The computer system 520 may be part of the central processing system of the microscope 510, and / or the computer system 520 may be part of a sub-component of the microscope 510, such as a sensor, actuator, camera, or illumination unit of the microscope 510.

[0143] Computer system 520 may be a local computer device (e.g., a personal computer, laptop, tablet, or mobile phone) having one or more processors and one or more storage devices, or it may be a distributed computer system (e.g., a cloud computing system having one or more processors and one or more storage devices distributed in different locations, such as at local clients and / or at one or more remote server clusters and / or data centers). Computer system 520 may include any circuitry or combination of circuitry. In one embodiment, computer system 520 may include one or more processors that can be of any type. As used herein, a processor may refer to any type of computing circuitry, such as, but not limited to, a microprocessor, microcontroller, complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, graphics processor, digital signal processor (DSP), multi-core processor, field-programmable array (FPGA), such as a processor for a microscope or microscope component (e.g., a camera), or any other type of processor or processing circuitry. Other types of circuitry that may be included in computer system 520 may be custom circuitry, application-specific integrated circuits (ASICs), etc., such as one or more circuitry (e.g., communication circuitry) for wireless devices such as mobile phones, tablets, laptops, two-way radios, and similar electronic systems. Computer system 520 may include one or more storage devices, which may include one or more storage elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard disk drives, and / or one or more drives that process removable media (such as optical discs (CDs), flash memory cards, digital video discs (DVDs), etc.). Computer system 520 may also include a display device, one or more speakers, and a keyboard and / or controller, which may include a mouse, trackball, touchscreen, voice recognition device, or any other device that allows system users to input information into and receive information from computer system 520.

[0144] Some or all of the method steps may be performed by (or using) hardware devices (e.g., processors, microprocessors, programmable computers, or electronic circuits). In some embodiments, one or more of the most important method steps may be performed by such devices.

[0145] Depending on certain implementation requirements, embodiments of the present invention can be implemented in hardware or software. Non-transitory storage media, such as floppy disks, DVDs, Blu-ray discs, CDs, ROMs, PROMs, EPROMs, EEPROMs, or FLASH memories, can be used to perform the embodiments, storing electronically readable control signals that cooperate (or are capable of cooperating with) a programmable computer system to cause the corresponding methods to be executed. Therefore, the digital storage medium can be computer-readable.

[0146] Some embodiments of the invention include a data carrier having electronically readable control signals that are capable of cooperating with a programmable computer system to enable the execution of one of the methods described herein.

[0147] Typically, embodiments of the present invention can be implemented as a computer program product having program code that, when run on a computer, is operable to perform one of the methods described above. The program code may, for example, be stored on a machine-readable medium.

[0148] Other embodiments include a computer program for performing one of the methods described herein, stored on a machine-readable medium.

[0149] In other words, therefore, one embodiment of the present invention is a computer program having program code that, when run on a computer, performs one of the methods described above.

[0150] Therefore, another embodiment of the invention is a storage medium (or data carrier, or computer-readable medium) having a computer program stored thereon for performing one of the methods described herein when executed by a processor. Data carriers, digital storage media, or recording media are generally tangible and / or non-transitory. Another embodiment of the invention is the apparatus described herein, which includes a processor and a storage medium.

[0151] Therefore, another embodiment of the invention represents a sequence of data streams or signals for performing one of the methods described herein. The sequence of data streams or signals may, for example, be configured to be transmitted via a data communication connection (e.g., via the Internet).

[0152] Another embodiment includes a processing means, such as a computer or programmable logic device, configured or adapted to perform one of the methods described herein.

[0153] Another embodiment includes a computer that has a computer program installed for performing one of the methods described herein.

[0154] Another embodiment of the invention includes an apparatus or system configured to transmit (e.g., electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may include, for example, a file server for transmitting the computer program to the receiver.

[0155] In some embodiments, a programmable logic device (e.g., a field-programmable array) may be used to perform some or all of the functions of the methods described herein. In some embodiments, the field-programmable array may cooperate with a microprocessor to perform one of the methods described herein. Generally, the methods are preferably performed by any hardware device.

[0156] As used herein, the term “and / or” includes any and all combinations of one or more of the related listed items and may be abbreviated as “ / ”.

[0157] Although some aspects have already been described in the context of the apparatus, it is clear 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, item, or feature of the corresponding apparatus.

[0158] Reference Symbol List

[0159] 100 images

[0160] 110 samples

[0161] 112 First Left Layer

[0162] 114 Edge

[0163] 116 Second Left Layer

[0164] 118 stripes

[0165] 120 Central Layer

[0166] 122 Edge

[0167] 124 Edge

[0168] 130 First right side layer

[0169] 132 Edge

[0170] 134 Second right side layer

[0171] 136 stripes

[0172] 140 points

[0173] 142 points

[0174] 144 points

[0175] 150 points

[0176] 152 points

[0177] 154 points

[0178] 160 distance

[0179] 162 Distance

[0180] 164 Distance

[0181] 200 images

[0182] 210 Area or section

[0183] 212 Area or section

[0184] 214 Area or section

[0185] 216 Area or section

[0186] 220 Area or section

[0187] 222 Area or section

[0188] 224 Area or section

[0189] 226 Area or section

[0190] 230 distance

[0191] 232 Distance

[0192] 234 Distance

[0193] 236 Distance

[0194] 300 images

[0195] 310 centerline

[0196] 320 distance

[0197] 322 Distance

[0198] 324 distance

[0199] 326 Distance

[0200] 328 Distance

[0201] 402 Rising branch of the protruding edge

[0202] 404 Descending branch of the protruding edge

[0203] 410 Protrusion Point

[0204] 412 Protrusion Point

[0205] 414 Protrusion Point

[0206] 416 Protrusion Point

[0207] 420 Protrusion Layer Region

[0208] 422 Centerline of the protrusion layer

[0209] 430 distance

[0210] 432 Distance

[0211] 434 Boundary line of the protruding layer region

[0212] 500 system

[0213] 510 Microscope

[0214] 520 Computer

Claims

1. An imaging device for determining the thickness of layers in an image, configured to: - Obtain an image (100) of a sample with one or more layers (112, 116, 120, 130, 134); - Obtain first estimated information (140, 142, 144) of the position of the first edge (122) of the indicator layer from the user interface. - Obtain second estimated information (150, 152, 154) of the position of the second edge (124) of the indicator layer from the user interface. - Determine the first edge of the layer (122) based on the first estimation information; - Determine the second edge (124) of the layer based on the second estimation information; and - Determine the distance between the first edge and the second edge (160, 162, 164).

2. The imaging device according to any one of the preceding claims, in, The first estimation information and / or the second estimation information may include one or more of the following: - One or more points (140). - One or more lines (210), or, - One or more regions (210).

3. The imaging device according to any one of the preceding claims, in, The estimated information can include the area (210, 212, 214, 216) provided by the brush tool on the user interface.

4. The imaging device according to claim 3, configured as follows: - Adjust the parameters of the brush tool, especially one or more of the following: -- Width of the brush stroke; -- The color of the brushstrokes; and -- Opacity of the brush stroke.

5. The imaging device according to any one of the preceding claims, configured as follows: - Based on the first and / or second estimation information, determine the margins (122, 124) according to one or more of the following: -- The intensity difference within the corresponding estimated information; -- Spectral differences within the corresponding estimated information; -- Color difference within the corresponding estimated information; and -- Applied to the corresponding estimation information, especially to the first and second estimation information, by trained machine learning algorithms.

6. The imaging device according to any one of the preceding claims, configured as follows: - If the determination of the first edge and / or the second edge generates multiple first edge candidates or multiple second edge candidates, the first edge is automatically determined based on the first edge candidates, and / or the second edge is automatically determined based on the second edge candidates.

7. The imaging device according to claim 6, configured as follows: - Define the first edge and / or the second edge (122, 124) as one or more of the following: -- Edge candidates with the maximum length; -- Along the edge candidates that determine the maximum intensity change; and -- Candidates along the edge with the largest sum of their intensity changes.

8. The imaging device according to any one of the preceding claims, configured as follows: - If the determination of the first edge and / or the second edge (122, 124) generates multiple first edge candidates or multiple second edge candidates: -- Display edge candidates in the user interface; -- Obtain user information related to one or more edge candidates; -- Determine the first edge and / or the second edge based on the acquired user information.

9. The imaging device according to any one of the preceding claims, configured as follows: - Divide the first edge (122) and / or the second edge (124) into multiple segments (210, 212, 214, 216, 220, 222, 224, 226); and - Determine the layer distance for each segment (230, 232, 234, 236).

10. The imaging device according to any one of the preceding claims, configured as follows: - Identify multiple distance candidates at different locations between the first and second edges; and - Determine the distance as the candidate distance with the shortest distance.

11. The imaging device according to claim 10, configured as follows: - Identify multiple additional distance candidates near the previously determined distance; and - The second distance is identified as another distance candidate with the shortest distance.

12. The imaging device according to any one of the preceding claims, configured as follows: - Identify the protrusions at the first edge and / or the second edge; and - Determine the distance based on one or two protruding points.

13. The imaging device according to any one of the preceding claims, configured as follows: - Determine the centerline (310) of the layer based on the determined first and second edges (122, 124); and - Determine one or more distances (320, 322) for the layer, where, A distance is defined as a line perpendicular to the center line between the two edges.

14. The imaging device according to any one of the preceding claims, configured as follows: - Determine the convex side (420) of the first edge and / or the second edge (410) and / or the portion of the convex side (420) of the first edge and / or the second edge (410); and - Determine the distance between opposite sides of the corresponding edge (430, 432).

15. The imaging device according to any one of the preceding claims, configured as follows: - Define multiple thicknesses for the layers; and - Determine one or more of the following: -- Maximum thickness of the layer; -- Minimum thickness of the layer; -- The average thickness of the layer; and -- Parameters indicating changes in the thickness of the indicative layer.

16. A computer-based method for determining the thickness of layers in an image, comprising the following steps: - Obtain images of samples with one or more layers (100); - Obtain first estimated information about the position of the first edge of the indicator layer from the user interface (140); - Obtain second estimated information (150) about the location of the second edge of the indicator layer from the user interface; - Determine the first edge of the layer (122) based on the first estimation information; - Determine the second edge (124) of the layer based on the second estimation information; and - Determine the distance (160) between the first edge and the second edge.

17. A computer program having program code, which, when run on a processor, performs the method according to the preceding claims.

Images