Devices and methods for measuring layer thickness

Abstract

This invention provides an imaging device that improves the measurement of layer thickness within an image. [Solution] The imaging device acquires an image 100 of a sample having one or more layers, obtains first estimation information 140, 142, 144 indicating the position of a first edge 122 of a layer from the user interface, obtains second estimation information 150, 152, 154 indicating the position of a second edge of a layer from the user interface, identifies the first edge of a layer based on the first estimation information, identifies the second edge of a layer based on the second estimation information, and identifies the distances 160, 162, 164 between the first edge and the second edge.

Description

Technical Field

[0001] The present disclosure relates to a device for measuring the thickness of a sample layer and a method for operating the device for thickness measurement.

[0002] Background Art In imaging, such as microscopic analysis of a sample, the thickness of the sample or one or more layers within the sample can be an important parameter. In current products on the market, users are required to manually identify multiple points at which distances are measured. These operations are time-consuming and complex and may require extensive manual input and user knowledge. There are also multiple automatic measurement methods, but incorrect measurement points are often identified by these automatic measurement methods, resulting in distances that do not represent the thickness of the desired layer being measured. Improvements in layer thickness measurement are desired.

[0003] Summary of the Invention An object of the present disclosure is to improve layer thickness measurement in an imaging system.

[0004] This problem is solved by the disclosed embodiments, particularly as defined by the claims independent thereto. The dependent claims provide information about other embodiments. Various aspects and embodiments of these aspects are also disclosed in the summary of the invention and the following description, which present additional features and advantages.

[0005] A first aspect of the present disclosure is an imaging device for identifying the thickness of a layer included in an image, - obtaining an image of a sample having one or more layers, - obtaining first estimation information indicating the position of a first edge of the layer from a user interface, - obtaining second estimation information indicating the position of a second edge of the layer from a user interface, - identifying a first edge of the layer based on the first estimation information, - Based on the second estimated information, identify the second edge of the layer, - Relating to an imaging device configured to determine the distance between a first edge and a second edge.

[0006] Acquiring an image may include receiving and / or retrieving image information associated with that image. Additionally or alternatively, acquiring an image may include identifying this information based on other information received / retrieved.

[0007] The imaging device may be configured to inspect objects that are difficult to see with the naked eye. One embodiment of the imaging device may be a microscope, such as a wide-field or bright-field microscope, a transmitted light microscope, a reflected light microscope, a phase-contrast microscope, etc. Microscopes also include macroscopes and stereoscopes. Another embodiment of the imaging device is an endoscope, and yet another embodiment is an exoscopy.

[0008] The sample may be of any sample type that can be examined by this imaging device, and may be an organic or inorganic sample. Additionally or alternatively, the sample may contain any other elements or molecules. For example, the sample may be tissue or rock, or may consist of another material, such as plastic or metal or compounds thereof. The sample may be opaque or transparent, or in any state in between.

[0009] For one or more layers visible in an image captured by this imaging device, the image exhibits corresponding edge lines, and one of these edge lines may separate two adjacent layers from each other.

[0010] The user interface may be a component of the imaging device. Alternatively, the user interface may be separate from the imaging device and connected to it for data transmission. The user interface may include a display configured to show the user images acquired by the imaging device. The user interface may have an input module on which the user can input information, such as first estimated information and second estimated information. The user interface may include, for example, one or more of the following: a touch display, a pointing device (e.g., a computer mouse or trackball or trackpad, a computer keyboard, a stylus, etc.), an audio detection device, and / or a gesture detection device.

[0011] To identify a first and second edge of one of the layers shown in the image, the imaging device is configured to receive or acquire user input characterizing the first and second estimated information, respectively, through a user interface. For example, the user traces an edge, or at least one or more individual points located on each edge, and / or marks a shape that matches it. Additionally or alternatively, the user provides information unrelated to a single pattern in the image, i.e., based solely on the user's experience and / or on multiple imaged artifacts. An algorithm (which may be a software module) that is a component of the imaging device identifies the aforementioned layer edges based on the acquired first and second estimated information.

[0012] Identifying the first and / or second edges may be based on interpolation or extrapolation of user information. Additionally or alternatively, identification may be based on a model that computes edges based on user information. Additionally or alternatively, identification may be based on a machine learning algorithm, such as a neural network, that is supplied with user information about the first and / or second edges. The device can operate with a single image or with a live image stream. In the latter case, the device may be configured to obtain first and second estimation information for at least one image in the image stream, and then identify the first and second edges across the entire image stream.

[0013] The algorithm may include, in particular, edge recognition processing supported by information obtained from user input. These means enable edge detection even when at least some of the edge information is below the image noise level.

[0014] The imaging device is further configured to determine the distance between a first edge and a second edge, particularly by the algorithm already described above and / or by 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 layers in an image, as determined by the edges identified by the user.

[0015] This imaging device is user-friendly and / or can reduce human error in judgment, which is advantageous in identifying layer edges and, consequently, layer thickness. This is because the user operation required to supply the necessary data to the imaging device is minimal, allowing the device, and especially its algorithms, to perform actions / calculations to identify layer edges and the distances between these layers. Therefore, layer edges and, consequently, layer thickness can be detected based on complementary human and automated knowledge.

[0016] The imaging device may selectively include components configured to provide the user with the measured distance (i.e., layer thickness), such as a visual and / or auditory display, printer, etc. This component may also be part of the user interface.

[0017] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, The first and / or second estimations include the following: -One or more points, - One or more lines, - Relating to imaging devices which may include one or more of the following regions.

[0018] The imaging device can be configured to acquire estimated information from the 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 may further, or instead, be configured to identify, i.e., interpolate and / or extrapolate, first layer edges and / or second layer edges based on the input points, lines, and / or regions, for example, by the algorithm already described above or another algorithm that is a component of the imaging device (which may be a software module). The user can input points, lines, and / or regions in a particularly simple way, for example by using a user interface and touch input. In this case, the points, lines, and / or regions are used as first and second estimated information, or first and second estimated information is generated based on the points, lines, and / or regions.

[0019] When layer edges are identified based on user-inputted information (points, lines, and / or regions), the user does not need to painstakingly identify the overall edges of the layers and supply the system with the curvature of the layer edges. Instead, the system (i.e., the imaging device) can identify the layer edges by indicating, starting from each point and / or region, that the lines shown in the image where the user has defined points and / or regions are indeed layer edge lines representing the layer edges in the image, and not any other fringes (non-edge lines) that may represent any other visible edge-like structures of the sample in the image. When layer edges are identified based on user-inputted lines, the user can indicate the precise curvature of the edge lines separating the layers, thereby facilitating the detection or identification of layer edges.

[0020] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, The estimated information relates to an imaging device that may include an area supplied by a brush tool via a user interface.

[0021] The brush tool is a virtual tool, for example, it appears on the user interface and / or the display of another user interface, and is understood to be a component of this imaging device or connected to this imaging device in the form of, for example, a mouse pointer symbol, touch input and / or the like. The brush tool indicates to the user that the brush marking mode for marking layer edges (especially for entering areas) is active. The aim is for the user to use the virtual brush tool to trace part or the entire layer edge. In doing so, the user marks at least one area surrounding each edge. That is, according to the embodiments described above, the brush tool can be used to provide one or more of a plurality of areas. The brush tool can provide first estimated information and / or second estimated information along an edge having large variations and / or especially a large number of curves. Nevertheless, the curvature of the layer edge can be specified particularly accurately.

[0022] One embodiment of the first aspect is an imaging device for specifying the thickness of a layer in an image, - related to an imaging device configured to adjust one or more of the parameters of the brush tool, especially -- the width of the brush stroke, -- the color of the brush stroke, -- and the opacity of the brush stroke.

[0023] Embodiments having these characteristics can have two general implementation forms.

[0024] In a first general implementation form, one or more brush stroke parameters are adjusted by the device (or the function of the device), whereby information regarding edge detection can be displayed to the user. For example, the user provides information regarding an edge to the brush tool, and depending on the result of edge identification, the brush stroke varies, i.e., - green for highly reliable edge detection results, - orange for low-reliability edge detection results, - colored red for no edge detection.

[0025] By setting parameters according to the result of the brush stroke, the user can be notified in a differentiated manner about the edge detection result and about locations where the user may wish to provide further information. In another implementation, the device is configured to adapt the width of the brush stroke (supplied by the user) after edge detection has been performed. This may be done such that the location where the edge was accurately found and the location where multiple edge candidates were found are indicated. For example, the width of the brush stroke is reduced to the detected edge (if an edge was found), and / or the width of the brush stroke is enlarged or reduced in the area where multiple edge candidates were found. All options are particularly applicable to the brush stroke supplied by one user, whereby all possible result types are shown to this user.

[0026] In a second general implementation form, one or more brush stroke parameters are adjusted by the user, and the adjustment information is received via the user interface. Thereby, the user can provide reduced information regarding an edge to the device. For example, the user can exclude other edges or edge-like structures (see above. Any other visible samples, any other stripes other than layer edges in the image representing the above) by adapting the size of the brush tool, particularly its width.

[0027] A brush stroke can also provide information about the likelihood of an edge in the area indicated by the brush tool. For example, the center of the brush stroke may provide a high likelihood of an edge being present in the indicated area, while the area outside the brush stroke may provide a low likelihood of an edge being present in the indicated area. In particular, the likelihood across the width of the brush stroke of a virtual brush may be distributed evenly or normally (for example, with the highest likelihood at the center of the brush stroke). Different user likelihoods in this context can also be provided by different colors of the brush stroke.

[0028] This allows for the rapid representation of edges within an image, either as input (from the user to the device) or as output (from the device to the user), both effectively and simultaneously.

[0029] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Based on the first estimated information and / or the second estimated information, --The difference in intensity within each estimated piece of information, --The spectral difference within each estimated piece of information, --Color differences within each estimated piece of information, -- relating to imaging devices configured to be identified based on one or more of the respective estimation information, particularly the first estimation information and the second estimation information, and the trained machine learning algorithms applied to them.

[0030] Therefore, the imaging device is configured to analyze data input by the user, i.e., each estimate, and as a result of this analysis, to determine and / or specify whether the first estimate and / or second estimate include image data and / or indicate that at least one section of a layer or layer edge exists within each estimate. Therefore, the imaging device may have an imaging processor configured to perform the above analysis, that is, to identify intensity differences, spectral differences and / or color differences between two of a plurality of layers in an image that are adjacent to each other and separated by layer edges and indicated by the user by providing the first estimate and the second estimate.

[0031] Additionally, or alternatively, the imaging device may have a machine learning algorithm capable of identifying two adjacent layers and consequently their common layer edges in the collected image data. This machine learning algorithm can be trained to recognize repeating patterns within a given brushstroke or region. In particular, this machine learning algorithm can be designed to perform edge recognition processing.

[0032] Therefore, layer edges can be automatically detected within the estimated information provided by the user.

[0033] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - The present invention relates to an imaging device configured such that, when the identification of a first edge and / or a second edge results in multiple candidates for the first edge or the second edge, the first edge is automatically identified based on the first edge candidate and / or the second edge is automatically identified based on the second edge candidate.

[0034] For example, the imaging device is configured to automatically select one of several first edge candidates as the first layer edge. Alternatively or additionally, the imaging device is configured to automatically select one of several second edge candidates as the second layer edge. Alternatively or additionally, the imaging device may be configured to automatically select the first edge by merging two or more of several first edge candidates, and (alternatively or additionally) to automatically select the second edge by merging two or more of several second edge candidates. Thus, edges are selectable without user intervention. Additionally or alternatively, the device can select from several edge candidates, so that only a small number of candidates are merged into the layer edges and / or only one candidate is selected as the layer edge. This selection can be achieved by including another software-based module that performs candidate reduction, i.e., selection. This module may include a machine learning algorithm trained to select one or more edges from several edge candidates.

[0035] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - The first edge and / or the second edge, -- Edge candidates with the longest length, --An edge candidate, where the maximum intensity change is identified along this edge candidate, --This relates to an imaging device configured to identify one or more edge candidates, including an edge candidate along which the sum of intensity changes is maximum.

[0036] The imaging device may be configured to automatically select one of the identified edge candidates and classify it as a layer edge based on its length. In this case, the imaging device can indicate the longest edge candidate (i.e., the edge candidate with the maximum length) as the actual layer edge. Alternatively or additionally, the imaging device may be configured to automatically select one of the identified edge candidates and classify it as a layer edge based on the intensity change between layers separated by the edge candidate. 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. Alternatively or additionally, the imaging device may be configured to select one of the identified edge candidates and classify it as a layer edge based on the sum of the intensity changes along each edge candidate. In other words, the imaging device identifies one of the edge candidates along which the sum of the intensity changes is maximum. This identified edge candidate is classified by the imaging device as the actual edge layer. This allows one or more edges to be detected particularly accurately and reasonably. Furthermore, this identification can be used as a validation check to verify that the selected edge candidate is indeed an edge and not merely a sample structure that could be confused with a layer edge.

[0037] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - If identifying the first edge and / or the second edge results in obtaining multiple candidates for the first and second edges, --Display edge candidates in the user interface,--Retrieve user information related to one or more edge candidates, --Regarding an imaging device configured to identify a first edge and / or a second edge based on acquired user information.

[0038] Therefore, user experience-based knowledge can be used in an advantageous way, particularly to avoid ambiguity when identifying the edges of actual layers, and to eliminate such ambiguity regarding other specific processes. In particular, acquired user information can be used to train machine learning algorithms, thereby further improving their ability to automatically distinguish non-edge structures from the edges of actual layers shown in an image.

[0039] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Divide the first edge and / or the second edge into multiple segments, - Regarding imaging devices configured to determine the layer distance for each segment.

[0040] The segments may be the same size, i.e., the same length. Alternatively, the segments may be of different sizes / lengths. Further in this embodiment, it is conceivable that the layer edge is divided into some segments of equal size and some segments of unequal size. Generally, the distances between different distance estimates may be pre-provided by the user and / or adjusted online by the user, for example, using a mouse.

[0041] This imaging device is configured to measure the distance between each identified segment, thereby providing distance estimates for different parts of the layer between the segmented edge layers.

[0042] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Identify multiple distance candidates at different positions between the first edge and the second edge, -The present invention relates to an imaging device configured to identify distances as distance candidates having the shortest distance.

[0043] The imaging device may be configured to measure the distance between layer edges at multiple points on an edge and identify the shortest of the measured distance candidates. The imaging device may further be configured to provide the user with the shortest distance, for example, through a user interface, display, etc. In this way, the global minimum value of the distance between edges, i.e., the layer thickness, can be determined in a particularly efficient manner.

[0044] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Identify several more distance candidates near the previously identified distance, -The present invention relates to an imaging device configured to identify a second distance as another distance candidate having the shortest distance.

[0045] In accordance with this embodiment, in order to improve the results obtained by identifying multiple distance candidates at different locations between the first edge and the second edge, and identifying the shortest of the distance candidates as the global minimum of the layer thickness, the imaging device may be configured to measure another distance in the vicinity of the distance candidate representing the global minimum of the layer thickness.

[0046] For example, one or more alternative distance candidates may be identified that are greater than the previously identified distance, or conversely or additionally, one or more alternative distance candidates may be identified that are less than the previously identified distance. In this case, the imaging device may further be configured to identify the shortest of the alternative distance candidates and to classify this shortest as another shortest distance between layer edges, i.e., as another global minimum of layer thickness. Both the step of identifying alternative distance candidates and the step of identifying the shortest of the alternative distance candidates are particularly repeatable until no shorter distance candidates are found between layer edges. As a result of this embodiment, the overall global minimum of layer thickness can be identified with particular accuracy.

[0047] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Identify the protrusion on the first edge and / or the second edge, - Relating to imaging devices configured to determine distance based on one or two protruding points.

[0048] This is advantageous because the shortest distance between the edge of the first layer and the edge of the second layer can arise from a protrusion with higher likelihood. This may also be the global minimum of the layer thickness. To determine each protrusion, the imaging device may be configured to identify the point on each layer edge that extends furthest in the direction of the opposite layer edge. Additionally or alternatively, edge protrusions can be identified by identifying the edge and then analyzing the spectrum along the edge line. High frequency may indicate a protrusion from which distance measurements can be initiated.

[0049] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Based on the identified first and second edges, identify the centerline of the layer. - Relating to an imaging device configured to identify one or more distances in a layer and one distance as a line between two edges perpendicular to a centerline.

[0050] The centerline can be identified at the midpoint between two layer edges. For example, it can be calculated as a piecewise straight line connecting all the center points between the two layer edges, or as a non-straight line.

[0051] To determine the distance, a connecting line can be identified that is perpendicular to the centerline and intersects both edges. This line may be bisected by the centerline. The connecting line contains two bisectors, which are arranged in a straight line and may each be located between one of the edges and the centerline. The length of the connecting line (i.e., the sum of the lengths of the two bisectors) is the distance between the two edges.

[0052] Since different connection lines may be identified, there may be multiple distances between edges. This method can be particularly efficient for determining the shortest distance between edges. All this method requires is to select or output the shortest distance identified from all the distances obtained.

[0053] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Identify the convex side of the first edge and / or the second edge, and / or the convex side of a portion of the first edge and / or the second edge, - Relating to an imaging device configured to determine the distance between opposite sides of each edge.

[0054] This embodiment may be useful for measuring curvature or distance within one of the first and / or second layer edges, for example, to examine a layer protrusion that intrudes into an adjacent layer. Each protruding layer edge has a shape such as substantially U-shaped, V-shaped, Ω-shaped, or parabolic, and surrounds the protruding layer region. According to this embodiment, the imaging device is configured to identify a first protrusion point and at least a second protrusion point at each layer edge, the first protrusion point located on the ascending branch of the layer edge, and the second protrusion point located on the 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 identifying the distance between opposite sides or branches of the protruding edge layer or portion of the edge layer. Optionally, the processing according to the above-described embodiment can be used to plot the centerline of the protrusion, i.e., the convex side of each layer edge, to identify the extent of the protruding layer region relative to the centerline. Another option is to define the boundary of the protruding layer region, where the distance between the opposite side or branch of the protruding edge layer and the boundary is parallel to each other.

[0055] One embodiment of the first aspect is an imaging device for determining the thickness of a layer in an image, - Multiple thicknesses are identified for each layer, -Next, that is, --Maximum thickness of the layer, --Minimum layer thickness, --Average thickness of the layer, --This relates to an imaging device configured to identify one or more parameters that indicate changes in layer thickness.

[0056] This provides the user with a comprehensive overview of the layer's dimensions and / or shape. This parameter characterizing the thickness change can, for example, indicate whether the layer's thickness increases or decreases along a specified or configurable reference direction of the layer.

[0057] A second aspect of this disclosure is a method for determining the thickness of a layer in an image, - The step of obtaining an image of a sample having one or more layers, - A step of obtaining first estimated information indicating the position of the first edge of the layer from the user interface, - A step of obtaining second estimated information from the user interface that indicates the position of the second edge of the layer, - A step of identifying the first edge of the layer based on the first estimated information, - A step of identifying the second edge of the layer based on the second estimated information, - A method comprising the step of determining the distance between a first edge and a second edge.

[0058] This method may be computer-implemented. Its steps can be performed by a computer system. Using this method, edges can be detected based on complementary human and automated knowledge. In particular, this method can be paused at specific points to await and / or receive user input.

[0059] A third aspect of this disclosure relates to a computer program having computer code for carrying out the method described above when the computer program is executed on a processor.

[0060] Other advantages and features are derived from the following embodiments, some of which refer to the drawings. The drawings do not necessarily show embodiments to scale. Dimensions of various features may be enlarged or reduced, particularly for the sake of clarity in the description. For this purpose, the drawings are at least partially schematic. [Brief explanation of the drawing]

[0061] [Figure 1] This figure shows a distance measurement according to one embodiment of the present disclosure. [Figure 2]This figure shows another distance measurement according to one embodiment of the present disclosure. [Figure 3] This figure shows yet another distance measurement according to one embodiment of the present disclosure. [Figure 4] This figure shows yet another distance measurement according to one embodiment of the present disclosure. [Figure 5] This figure shows a microscope system for an embodiment of the present disclosure.

[0062] While several aspects have been described in the context of the apparatus (or system) in this disclosure, the descriptions of these aspects also represent descriptions of the corresponding methods, where a block or device corresponds to a step or a feature of a step.

[0063] Similarly, the aspects described in the context of the steps also represent a description of the corresponding device, or the corresponding block, item, or feature of the system, which may in particular be distributed across different locations and configured to exchange information between these different locations by their respective means of communication.

[0064] Generally, the disclosure of the described method also applies to corresponding devices (or apparatus) for carrying out the method, or corresponding systems comprising one or more devices, and vice versa. For example, if a particular step is described, the corresponding device may include the features necessary to perform the described step, even if the features are not explicitly described or shown in the diagram. On the other hand, if, for example, a particular device is described based on a functional unit, the corresponding method may include one or more steps to perform the described function, even if these steps are not explicitly described or shown in the diagram. Similarly, a system may be provided with corresponding device features or features for performing a particular step. The various exemplary aspects and features of embodiments described above or below are combinable unless otherwise explicitly stated.

[0065] As used herein, the terms “and / or” include all possible combinations of one or more of the items relating to the description and may be abbreviated as “ / ”. Expressions such as “exemplary,” “for example,” or “especially” indicate conditional or optional features that can be combined with all other (essential, conditional, or optional) features of the aspects or embodiments of the present disclosure, unless otherwise expressly stated.

[0066] The following description refers to accompanying drawings that illustrate certain aspects that form part of the disclosure and that enable understanding of the disclosure. The same reference numerals refer to the same or at least functionally or structurally similar features.

[0067] Detailed explanation Figures 1, 2, and 3 show images 100, 200, and 300 illustrating a sample 110 of an object under inspection, which has multiple layers 112, 116, 120, 130, and 134. Images 100, 200, and 300 were captured by an imaging device, specifically by a microscope 510 (see Figure 5).

[0068] Image 100 may be a still image (like a photograph) or a video stream playback. In this embodiment, five layers are visible in Image 100: the first left layer 112, the second left layer 116, the central layer 120, the first right layer 130, and the 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 central layer 120 are separated from each other by edge 122, the central 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, the layer edges 114, 122, 124, and 132 are represented by their respective layer edge lines. Furthermore, non-edge lines or streaks are indicated in Image 100 by reference numbers 118 and 136. The streaks 118 and 136 visible in Image 100 result from the edge-like structure of the object being inspected.

[0069] Each imaging device or microscope 510 is configured to perform a method for determining the thickness of one or more of the layers 112, 116, 120, 130, and 134 visible in the image 100. The method will be illustrated below, primarily with reference to the central layer 120. It should be understood that the method is equally applicable to one or two of the layers 116 and 130, since these layers are sandwiched between edges 114 and 122, and between edges 124 and 132, respectively.

[0070] Therefore, images 100, 200, and 300 of sample 110 are acquired by this imaging device or microscope 510, and sample 110 contains layers 112, 116, 120, 130, and 134. In addition, first and second estimation information is obtained from the user interface, the first estimation information indicating the location of the first edge of one of the layers 112, 116, 120, 130, and 134, and the second estimation information indicating the location of the second edge of the same layer 112, 116, 120, 130, and 134. For example, if the user wants to obtain information about the central layer 120, the user will provide first and second estimation information regarding this central layer 120, i.e., regarding the locations of layer edges 122 and 124, since the central layer 120 is adjacent to edges 122 and 124. Furthermore, the method includes the steps of identifying a first edge of the central layer 120, in this case edge 122, based on first estimation information, and identifying a second edge 124 of the central layer 120, based on second estimation information. In this embodiment, the imaging device also identifies the location and curvature of edges 122 and 124, provided that the user provides first and second estimation information characterizing edges 122 and 124, respectively. The method further includes the step of determining the distance between the first edge, in this case edge 122, and the second edge, in this case edge 124.

[0071] As can be seen from Figure 1, which illustrates distance measurement according to one embodiment of the present disclosure, the first estimation information and / or the second estimation information may include one or more points. The user provides points 140, 142, and 144 located on the first edge 122 of the central layer 120, respectively. Additionally, the user provides the locations of points 150, 152, and 154 located on the second edge 124 of the central layer 120, respectively. Consequently, at least a portion of the edges 122 and 124 are known to the system, i.e., the imaging device or microscope 510. To determine one or more distances between edges 122 and 124, the distance 160 between edge point 140 and edge point 150, the distance 162 between edge point 142 and edge point 152, and the distance 164 between edge point 144 and edge point 154 are measured, respectively. Distances 160, 162, and 164 are visualized in image 100 by their respective connecting lines. In one embodiment, one of the points 140, 142, and 144 is identified as a protrusion, and / or one of the points 150, 152, and 154 is identified, and distances are determined based on the protrusion points. Additionally or instead, the first and / or second estimation information may include one or more lines, for example, lines that the user has created by tracing the respective edges 122, 124. One of these lines may run, for example, between point 140 and point 142, or between point 152 and point 154, etc.

[0072] In one embodiment, the first and / or second estimations used to identify edge 122 and / or edge 124 are identified based on intensity, spectral differences, and / or color differences between layers 116 and 120 and / or between layer 120 and layer 130, which are adjacent to each other in image 100 and separated by edges 122 and 124, respectively. Additionally or alternatively, a trained or trainable machine learning algorithm can be applied to the first and second estimations.

[0073] According to one embodiment, multiple thicknesses for layer 120 are specified, including the maximum thickness of layer 120, the minimum thickness of layer 120, and / or the average thickness of this layer. Alternatively or additionally, a parameter indicating a change in the thickness of layer 120 is specified. This parameter characterizing the thickness change can, for example, indicate whether the thickness of layer 120 increases or decreases along a specified or configurable reference direction of layer 120, for example, along its longitudinal extension direction.

[0074] One or more embodiments described herein allow for the identification of multiple distance candidates at various locations between the first edge 122 and the second edge 124. In this case, the distance candidate having the shortest distance is identified as the distance between layer edge 122 and layer 124. In Figure 1, the shortest distance candidate may initially be, for example, distance 162. Further visible in Figure 1 is that, according to one embodiment, several more distance candidates are identified, each of which is located near the previously identified distance 162. Subsequently, a second distance is identified as another distance candidate having the shortest distance. Referring to Figure 1, the shortest of the other distance candidates results in distance 162 being the second distance between edge 122 and edge 124.

[0075] Figure 2 illustrates another distance measurement according to one embodiment of the present disclosure. As can be seen in Figure 2, the first and / or second estimation information may include one or more regions, in which regions 210, 212, 214, 216, 220, 222, 224, and 226 are illustrated in image 200. In Figure 2, regions 210, 212, 214, 216 and regions 220, 222, 224, and 226 each enclose one section or segment of edge 122 and edge 124, respectively. To determine one or more distances between edge 122 and edge 124, distances 230, 232, 234, and 236 are measured between region 210 and region 220, between region 212 and region 222, between region 214 and region 224, and between region 216 and region 226, respectively. For example, the distance 230 between two corresponding regions 210 and 220, the distance 232 between region 212 and region 222, the distance 234 between region 214 and region 224, and the distance 236 between region 216 and region 226 can each be measured between the midpoints of the regions.

[0076] According to one embodiment, this applies to the present method, in which a brush tool is used and one or more of the regions 210, 212, 214, 216, 220, 222, 224, 226 can be provided by the user as first or second estimated information. Thus, the user wields the brush tool, which is a virtual data input tool provided in the user interface, at least roughly along the edges to be marked. Since the brush tool is wider than the lines representing the edges, it is easy for the user to cover each edge with a brush stroke. According to one embodiment, the width of the brush tool can be adapted to the requirements of the image 200, such as its scale factor and / or magnification, in order to avoid including non-edge elements while the brush stroke is being performed.

[0077] According to one embodiment, a first edge 122 and / or a second edge 124 are divided into multiple segments of the same or different sizes. To determine one or more distances between edge 122 and edge 124, the respective distances between one segment of the first edge 122 and one segment of the second edge 124 are measured.

[0078] Figure 3 shows another distance measurement according to one embodiment of the present disclosure. In Figure 300, it can be seen that a center line 310 is identified, which is precisely centered between two layer edges 122 and layer edge 124 along the curvature. To identify one or more distances between edges 122 and 124, connecting lines are identified that are perpendicular to the center line 310 and intersect the two edges 122 and 124. Each connecting line is bisected by the center line 310; that is, each connecting line consists of two linearly aligned bisectors. Each connecting line also represents the respective distances 320, 322, 324, 326, and 328 between edges 122 and 124.

[0079] Figure 4 shows distance measurement according to one embodiment of the present disclosure, in which a convex projection section of the first edge 122 is inspected. Image 400 shown in Figure 4 is, for example, a magnified view of sample 110, in which only layers 116, 120 and the intermediate edge 122 are visible. As can be seen in Image 400, the projection section of the first edge 122 includes an ascending branch 402 and a descending branch 404. This projection section surrounds a projection layer region 420 that protrudes from layer 120 and thus enters layer 116. In this embodiment, first projection points 410, 412 and second projection points 414, 416 are identified. The first projection points 410, 412 are located on the ascending branch 402, while the second projection points 414, 416 are located on the descending branch 404 of edge 122. To examine the protruding layer region 420, the distance 430 between the first protruding point 412 and the first protruding point 414, and the distance 432 between the second protruding point 410 and the second protruding point 416 can be measured. In other words, according to this embodiment, the distances between opposite sides of the edge 122 or between the branches 402, 404 are identified. Additionally or instead, the protruding layer centerline 422 can be identified, thereby allowing it to be used as described above in relation to Figure 3. Furthermore, as can be seen in Figure 4, it is also possible to identify the boundary line 434 of the protruding layer region 420, which is surrounded by the convex protruding section of the first edge 122 and this boundary line 434. In this case, the distances 430 and 432 can be aligned parallel to the boundary line 434.

[0080] If the identification of edges 122 and 124 results in a plurality of first edge candidates and / or second edge candidates, one embodiment achieves that one or both of edges 122 and 124 are automatically identified based on the corresponding first or second edge candidate, respectively. Alternatively or additionally, the first edge 122 can be automatically identified by combining two or more first edge candidates, and (alternatively or additionally) the second edge 124 can be automatically identified by combining two or more second edge candidates.

[0081] Again, we refer to the case where identifying edges 122 and 124 results in obtaining multiple first edge candidates and / or second edge candidates. In this case, in one embodiment, the first edge 122 and / or the second edge 124 can be identified as one of the corresponding edge candidates having the longest length, as one of the corresponding edge candidates along which the greatest intensity change is identified, and / or as one of the corresponding edge candidates along which the sum of intensity changes is maximized.

[0082] Furthermore, if the identification of edges 122 and 124 results in multiple first edge candidates and / or second edge candidates, in one embodiment, the edge candidates are displayed by the user interface. Subsequently, user information is obtained for one or more edge candidates, and based on the obtained user information, the first edge 122 and / or second edge 124 are identified.

[0083] Some embodiments relate to microscopes that include systems such as those described in relation to one or more of Figures 1 to 3. Alternatively, the microscope may be part of a system such as those described in relation to one or more of Figures 1 to 4, or may be connected to a system such as those described in relation to one or more of Figures 1 to 4.

[0084] Figure 5 shows a schematic diagram of a system 500 configured to carry out the method described herein. The system 500 includes a microscope 510 and a computer system 520. The microscope 510 is configured to take images and is connected to the computer system 520. The computer system 520 is configured to carry out at least part of the method described herein. The computer system 520 may be configured to run machine learning algorithms. The computer system 520 and the microscope 510 may be separate entities or may be integrated within a single 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 dependent component of the microscope 510, such as a sensor, actor, camera, or illumination unit of the microscope 510.

[0085] The computer system 520 may be a local computer device (e.g., a personal computer, laptop, tablet computer, 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 various locations such as local clients and / or one or more remote server farms and / or data centers). The computer system 520 may include any circuit or combination of circuits. In one embodiment, the computer system 520 may include one or more processors, which can be of any kind. As used herein, the processor may be intended to be any kind of computing circuit, such as a microprocessor for a microscope or microscopic component (e.g., a camera), a microcontroller, a composite 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 multicore processor, a field-programmable gate array (FPGA), or any other kind of processor or processing circuit. Other types of circuits that may be included in the computer system 520 may be custom circuits, application-specific integrated circuits (ASICs), etc., such as one or more circuits (communication circuits, etc.) used in wireless devices such as mobile phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. The computer system 520 may also include one or more storage devices that may contain one or more memory elements suitable for a particular purpose, such as main memory in the form of random access memory (RAM), one or more hard drives and / or one or more drives that handle removable media such as compact discs (CDs), flash memory cards, digital video discs (DVDs), etc.The computer system 520 may also include a display device, one or more speakers and a controller which may include a keyboard and / or mouse, trackball, touchscreen, voice recognition device, or any other device which enables a user of the system to input information into and receive information from the computer system 520.

[0086] Some or all of the steps may be performed by a hardware device (or by using a hardware device), such as a processor, microprocessor, programmable computer, or electronic circuit. In some embodiments, one or more of the most critical steps may be performed by such a device.

[0087] Depending on certain implementation requirements, embodiments of the present invention may be implemented in hardware or software. This implementation is feasible using a non-transient recording medium, which is a digital recording medium, etc., that stores electronically readable control signals and cooperates (or can cooperate) with a programmable computer system to carry out each method. Examples include floppy disks, DVDs, Blu-rays, CDs, ROMs, PROMs and EPROMs, EEPROMs, or FLASH memory. Thus, the digital recording medium may be computer-readable.

[0088] Some embodiments of the present invention include a data carrier having electronically readable control signals that can cooperate with a programmable computer system so as to carry out any of the methods described herein.

[0089] Generally, embodiments of the present invention can be implemented as a computer program product comprising program code, which operates to perform one of the methods when the computer program product is executed on a computer. This program code may be stored, for example, on a machine-readable carrier.

[0090] Another embodiment includes a computer program stored in a machine-readable carrier for carrying out any of the methods described herein.

[0091] Therefore, in other words, embodiments of the present invention are computer programs having program code for carrying out any of the methods described herein when the computer program is executed on a computer.

[0092] Accordingly, another embodiment of the present invention is a recording medium (or data carrier or computer-readable medium) containing a stored computer program for carrying out any of the methods described herein when executed by a processor. The data carrier, digital recording medium, or recording medium is typically tangible and / or non-transient. Another embodiment of the present invention is an apparatus, such as those described herein, comprising a processor and a recording medium.

[0093] Therefore, another embodiment of the present invention is a data stream or signal sequence representing a computer program for carrying out any of the methods described herein. The data stream or signal sequence may be configured to be transmitted, for example, over a data communication connection, such as the Internet.

[0094] Another embodiment includes processing means, for example, a computer or programmable logic device configured or adapted to carry out any of the methods described herein.

[0095] Another embodiment includes a computer having an installed computer program for carrying out any of the methods described herein.

[0096] Another embodiment of the present invention includes an apparatus or system configured to transfer (e.g., electronically or optically) a computer program for carrying out any 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 transferring the computer program to the receiver.

[0097] In some embodiments, a programmable logic device (e.g., a field-programmable gate array) may be used to perform some or all of the functions of the methods described herein. In some embodiments, the field-programmable gate array may cooperate with a microprocessor to carry out any of the methods described herein. Generally, the methods are advantageously carried out by any hardware device.

[0098] As used herein, the term "and / or" includes all possible combinations of one or more of the related items and may be abbreviated as " / ".

[0099] While several embodiments have been described in the context of the apparatus, it is clear that these embodiments also represent descriptions of the corresponding methods, where blocks or apparatus correspond to steps or features of steps. Similarly, embodiments described in the context of steps also represent descriptions of the corresponding blocks, items, or features of the corresponding apparatus. [Explanation of Symbols]

[0100] 100 images 110 samples 112 The first left layer 114 Edge 116 The second left layer 118 Stripes 120 Central layer 122 Edge 124 Edge 130 The first right-hand layer 132 Edge 134 The second right-hand layer 136 Stripes 140 points 142 points 144 points 150 points 152 points 154 points 160 distance 162 distance 164 distance 200 images 210 regions or segments 212 regions or segments 214 regions or segments 216 regions or segments 220 regions or segments 222 regions or segments 224 regions or segments 226 regions or segments 230 distance 232 distance 234 distance 236 distance 300 images 310 Center line 320 distance 322 distance 324 distance 326 distance 328 distance 402 Ascending branch of projection edge 404 Descending branch of the projection edge 410 Projection point 412 Projection point 414 Projection point 416 Projection point 420 Projection layer area 422 Center line of protrusion layer 430 distance 432 distance 434 Boundary of the protruding layer region 500 Systems 510 Microscope 520 Computers

Claims

1. An imaging device for determining the thickness of layers in an image, A sample image (100) having one or more layers (112, 116, 120, 130, 134) is obtained. First estimated information (140, 142, 144) indicating the position of the first edge (122) of the layer is obtained from the user interface. Second estimated information (150, 152, 154) indicating the position of the second edge (124) of the layer is obtained from the user interface. Based on the first estimated information, the first edge (122) of the layer is identified. Based on the second estimated information, the second edge (124) of the layer is identified. An imaging device configured to determine the distances (160, 162, 164) between the first edge and the second edge.

2. The first estimation information and / or the second estimation information include: One or more points (140), One or more lines (210), or One or more regions (210) The imaging device according to claim 1, which may include one or more of the following.

3. The imaging device according to claim 1 or 2, wherein the estimation information may include regions (210, 212, 214, 216) supplied by a brush tool via a user interface.

4. One parameter of the aforementioned brush tool, in particular The width of the brush stroke, The color of the brush strokes, The opacity of the brush strokes, An imaging device according to any one of claims 1 to 3, configured to adjust one or more of the following.

5. The edges (122, 124) are determined based on the first estimation information and / or the second estimation information. The difference in intensity within each estimated piece of information, The spectral difference within each estimated piece of information, The color difference within each estimated piece of information, A trained machine learning algorithm applied to each of the estimated pieces of information, particularly the first estimated piece of information and the second estimated piece of information, An imaging device according to any one of claims 1 to 4, configured to identify based on one or more of the following.

6. The imaging device according to any one of claims 1 to 5, wherein if the identification of a first edge and / or a second edge results in a plurality of first edge candidates or second edge candidates, the device is configured to automatically identify a first edge based on the first edge candidates and / or a second edge based on the second edge candidates.

7. The first edge (122) and / or the second edge (124) Edge candidates with the longest length, An edge candidate, where the maximum intensity change is identified along this edge candidate, The imaging device according to claim 6, configured to identify one or more edge candidates, including an edge candidate along which the sum of intensity changes is maximum.

8. If the identification of the first edge (122) and / or the second edge (124) results in a plurality of first edge candidates or second edge candidates, The aforementioned edge candidates are displayed in the user interface. Retrieve user information related to one or more edge candidates, The imaging device according to any one of claims 1 to 7, configured to identify the first edge and / or the second edge based on the acquired user information.

9. The first edge (122) and / or the second edge (124) are divided into a plurality of segments (210, 212, 214, 216, 220, 222, 224, 226), An imaging device according to any one of claims 1 to 8, configured to specify the distances of the layers (230, 232, 234, 236) for each segment.

10. Multiple distance candidates are identified at different positions between the first edge and the second edge. An imaging device according to any one of claims 1 to 9, configured to identify the distance as a distance candidate having the shortest distance.

11. Further distance candidates are identified near the previously identified distance, The imaging device according to claim 10, configured to identify a second distance as another distance candidate having the shortest distance.

12. Identify the protruding point on the first edge and / or the second edge, An imaging device according to any one of claims 1 to 11, configured to determine the distance based on one or two protruding points.

13. Based on the identified first edge (122) and second edge (124), the center line (310) of the layer is identified. An imaging device according to any one of claims 1 to 12, configured to identify one or more distances in the layers (320, 322) and to identify one distance as a line between two edges perpendicular to the center line.

14. Identify the convex side (420) of the first edge and / or the second edge (410), and / or a portion of the convex side (420) of the first edge and / or the second edge (410), An imaging device according to any one of claims 1 to 13, configured to determine a distance (430, 432) between opposite sides of each of the aforementioned edges.

15. Multiple thicknesses are identified for the aforementioned layer, Next, that is, The maximum thickness of the aforementioned layer, The minimum thickness of the aforementioned layer, The average thickness of the aforementioned layer, Parameters that show the change in layer thickness, An imaging device according to any one of claims 1 to 14, configured to identify one or more of the following.

16. A computer-implemented method for determining the thickness of layers within an image, The steps include obtaining an image of a sample having one or more layers (100), The steps include obtaining first estimated information (140) indicating the position of the first edge of the layer from the user interface, The steps include obtaining second estimated information (150) indicating the position of the second edge of the layer from the user interface, A step of identifying the first edge (122) of the layer based on the first estimated information, The steps include identifying the second edge (124) of the layer based on the second estimation information, A method comprising the step of determining the distance (160) between the first edge and the second edge.

17. A computer program comprising program code for carrying out the method according to claim 16 when the computer program is executed on a processor.

Images