System, method and computer program for surgical microscope system and corresponding surgical microscope system

Abstract

To provide the improved concept for operating a surgical microscope system.SOLUTION: A system is configured so as to identify depth characteristics of a surgical site (10) imaged using a microscope (120). A system is configured so as to adjust the numerical aperture of the microscope (120) based on the depth characteristics of at least a part of the surgical site (10).SELECTED DRAWING: Figure 1b

Description

[Technical Field]

[0001] Examples relate to systems, methods and computer programs for surgical microscope systems and corresponding surgical microscope systems. [Background technology]

[0002] A microscope, such as a microscope used in a surgical microscope system, is an optical system that includes various optical components. One optical component of a microscope is the iris diaphragm, which is an adjustable opening that controls how much light reaches the microscope's eyepiece or optical imaging sensor. If the iris diaphragm opening is larger, more light passes through the iris diaphragm to reach the eyepiece or optical imaging sensor. This generally increases the resolution of the scene or image, but decreases the so-called depth of field, which is the distance distance at which the view of the specimen being observed or captured appears sharp. If the iris diaphragm opening is smaller, less light passes through. This improves depth perception but reduces resolution. In surgical microscopy, the iris diaphragm opening is generally not adapted to the situation, i.e., the surgical procedure being performed, and therefore may not provide the surgeon with an optimal view. Because manual adjustment can be an additional burden for the surgeon, the iris diaphragm position is typically fixed. Furthermore, manual adjustment can result in poor image quality if the iris diaphragm opening is improperly adjusted or forgotten.

[0003] Improved concepts for operating surgical microscope systems may be desirable. Summary of the Invention [Means for solving the problem]

[0004] The above needs are addressed by the subject matter of the independent claims.

[0005] The proposed concept is based on the insight that image quality, while often good enough in a given surgical situation, is inferior to the best image quality that the optical system can provide if the iris diaphragm setting is improved. To improve image quality, a trade-off between image resolution and depth of field can be made taking into account the current surgical scenario. In particular, since depth characteristics are a key factor in selecting a desired depth of field, a trade-off can be made taking into account the depth characteristics of the surgical site. Therefore, the iris diaphragm setting, i.e., the numerical aperture of the microscope, can be set to match the depth characteristics of the surgical site, i.e., to obtain a depth of field appropriate for the surgical scenario, resulting in improved image quality.

[0006] Various examples of the present disclosure relate to a system for a microscope in a surgical microscope system. The system includes one or more processors and one or more storage devices. The system is configured to identify depth characteristics of a surgical site to be imaged using the microscope. The system is configured to adjust the numerical aperture of the microscope based on the depth characteristics of at least a portion of the surgical site. As outlined above, this can improve image quality.

[0007] In various examples, the system may be configured to determine a depth of field of at least a portion of the surgical site and adjust the numerical aperture of the microscope based on the depth of field. A depth of field, which may be a desired depth of field covering at least a portion of the surgical site, may be derived from the depth characteristics. The numerical aperture of the microscope affects the depth of field of the view at the surgical site and is therefore adjustable based on the determined depth of field. As a result, the system may be configured to adjust the numerical aperture so that the depth of field provided by the microscope matches the depth of field of at least a portion of the surgical site.

[0008] Surgeons often use surgical microscopes for many consecutive hours each day. As a result, surgeons have personal preferences regarding microscope settings. For example, some surgeons prefer a wider depth of field, which allows portions of the field of view that are tilted upward or downward to be clearly visible, while some surgeons prefer to increase resolution by limiting the depth of field to a precise region of interest at the surgical site. Therefore, the system may be configured to adjust the numerical aperture so that the depth of field provided by the microscope better suits the personal depth of field preference of the surgeon using the surgical microscope system.

[0009] As described above, within a surgical site, certain regions may be of particular interest to a surgeon, i.e., the surgeon is currently operating in this region. The system may be configured to identify the region of interest within the surgical site. The system may be configured to adjust the numerical aperture of the microscope based on depth characteristics of the region of interest within the surgical site. Thus, when adjusting the numerical aperture of the microscope, other portions of the surgical site (which also appear within the field of view of the microscope) may be ignored.

[0010] There are various ways to identify the region of interest. For example, a system may be configured to acquire imaging sensor data from an optical imaging sensor of a microscope. The system may be configured to identify the region of interest based on the imaging sensor data. In particular, the system may be configured to perform image processing on the imaging sensor data to identify a portion of the surgical site where surgery is being performed and to identify the region of interest based on the portion of the surgical site where surgery is being performed. Alternatively or additionally, visual markers (such as a circle highlighting a portion of the surgical site), fluorescence, or object detection may be performed to identify tissue that is likely to be of interest to the surgeon. These visual cues may be used to define the region of interest without requiring additional involvement from the surgeon.

[0011] Alternatively or additionally, the system may be configured to identify the region of interest based on user input signals obtained via a user interface of the surgical microscope system, i.e., the region of interest may be defined manually by the surgeon (or an assistant), for example, via a touchscreen or pointing device.

[0012] In some examples, additional measurement hardware may be used to determine depth characteristics. For example, the system may be configured to acquire sensor data from a depth sensor of the surgical microscope system and determine depth characteristics of at least a portion of the surgical site based on the sensor data of the depth sensor. For example, a time-of-flight sensor (optical or ultrasound-based), a structured light sensor, or a separate pair of stereo photogrammetry sensors may be used for this purpose.

[0013] Image processing may be used to determine depth characteristics of the surgical site. For example, the system may be configured to acquire imaging sensor data from an optical imaging sensor of a microscope and determine depth characteristics of at least a portion of the surgical site based on the imaging sensor data. For example, techniques such as structured light-based depth measurement, numerical aperture sweeping, or focus sweeping may be used to determine the depth characteristics.

[0014] In some examples, different numerical aperture settings may be applied, and the resulting image quality may be compared to identify depth characteristics. In other words, a numerical aperture sweep may be performed. In this case, the system may be configured to sweep the numerical aperture of the microscope to generate multiple frames of imaging sensor data based on different numerical apertures, and to identify depth characteristics of at least a portion of the surgical site based on the multiple frames of imaging sensor data based on the different numerical apertures. If a decrease in numerical aperture (i.e., a decrease in the diameter of the aperture) does not significantly improve image quality, the depth of field of the microscope may match the depth profile of at least a portion of the surgical site. To identify image quality, the contrast and / or occurrence of high spatial frequencies in the image frames may be analyzed; the higher the contrast and spatial frequencies present, the more regions of the surgical site may be considered sharp in the imaging sensor data. Thus, the system may be configured to identify depth characteristics of at least a portion of the surgical site based on contrast and / or occurrence of spatial frequencies above a predetermined spatial frequency threshold in each frame of the multiple frames.

[0015] Image clarity is not only related to the numerical aperture, but also depends on whether the focal length / working distance used is appropriate. Therefore, the focal length / working distance may also be taken into consideration when determining a depth profile. The system may be configured to control a microscope or surgical microscope system to perform a sweep of the working distance and / or focal length of the microscope to generate a plurality of additional frames of imaging sensor data based on different working distances or focal lengths, and to determine depth characteristics of at least a portion of the surgical site based on the plurality of additional frames of imaging sensor data based on the different working distances or focal lengths. For example, the system may be configured to select a working distance or focal length based on the plurality of additional frames of imaging sensor data generated during the working distance or focal length sweep, and to sweep the numerical aperture of the microscope while using the selected working distance or focal length to generate a plurality of frames of imaging sensor data based on different numerical apertures. In other words, an appropriate focal length / working distance may first be set to ensure that the depth of field varies around an appropriate starting point, and then the numerical aperture may be adjusted based on the selected focal length / working distance.

[0016] Various examples of the present disclosure relate to a microscope and corresponding surgical microscope system that includes the system described above.

[0017] Various examples of the present disclosure relate to a corresponding method for a microscope of a surgical microscope system, the method including identifying a depth characteristic of a surgical site imaged using the microscope, the method including adjusting a numerical aperture of the microscope based on the depth characteristic of at least a portion of the surgical site.

[0018] Various examples of the present disclosure also relate to corresponding computer programs having program codes for performing the above-mentioned methods when the computer program runs on a processor.

[0019] Some examples of apparatus and / or methods are now described, by way of example only, and with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0020] [Figure 1a] FIG. 1 is a schematic diagram showing an example of a system for a microscope of a surgical microscope system connected to various components of the microscope. [Figure 1b] FIG. 1 is a schematic diagram illustrating an example of a surgical microscope system. [Figure 1c] 10 is a graph showing an example of a distribution of spatial frequencies in imaging sensor data. [Figure 1d] 1 is a graph showing an example grid of working distance / focal length and numerical aperture setting combinations. [Figure 1e] 1 is a graph showing an example grid of working distance / focal length and numerical aperture setting combinations. [Figure 1f] 1 is a graph showing the depth profile of a surgical site. [Figure 1g] This is a top view of the surgical site. [Figure 2] 1 is a flow chart illustrating an example of a method for a microscope of a surgical microscope system. [Figure 3] FIG. 1 is a schematic diagram illustrating the effect of an iris aperture on the depth of field of a microscope scene. [Figure 4] FIG. 1 is a schematic diagram illustrating an example of a system including a microscope and a computer system. DETAILED DESCRIPTION OF THE INVENTION

[0021] Various examples will now be described more fully with reference to the accompanying drawings, in which some examples are shown and in which the thickness of lines, layers and / or regions may be exaggerated for clarity.

[0022] 1a is a schematic diagram illustrating an example of a system 110 for a microscope of a surgical microscope system connected to various components of the microscope, such as an optical imaging sensor 122 and an iris diaphragm 124. The system 110 is tasked with controlling various aspects of the microscope and the entire surgical microscope system and / or processing various types of sensor data of the surgical microscope system. Thus, the system 110 may be implemented as a computer system that interacts with the various components of the surgical microscope system.

[0023] As shown in FIG. 1a, the system 110 includes one or more processors 114 and one or more storage devices 116. Optionally, the system further includes one or more interfaces 112. The one or more processors 114 are connected to the one or more storage devices 116 and the optional one or more interfaces 112. Generally, the functionality of the system is provided by the one or more processors in cooperation with one or more interfaces (e.g., for exchanging information with an optical imaging sensor 122 of the microscope, an iris diaphragm 124 of the microscope, a display device of the surgical microscope system, or a depth sensor of the surgical microscope system) and / or one or more storage devices (for storing and / or retrieving information). The system is configured to determine depth characteristics of a surgical site 10 imaged using the microscope. The system is configured to adjust a numerical aperture of the microscope based on at least a portion of the depth characteristics of the surgical site. For example, the numerical aperture may be adjusted by controlling the iris diaphragm 124 of the microscope.

[0024] FIG. 1a further highlights the resulting depth of field 130 and region of interest 140 of the surgical site 10, which will be introduced in more detail at a later stage.

[0025] The optical imaging sensor 122 and the iris diaphragm 124 are part of a microscope, such as the microscope 120 of the surgical microscope system 100 shown in FIG. 1b. Generally, a microscope, such as the microscope 120, is an optical instrument suitable for inspecting objects that are too small to be inspected by the human eye (alone). For example, a microscope can provide optical magnification of a sample, such as the sample 10 shown in FIGS. 1a, 1b, 1f, and 3. In modern microscopes, optical magnification is often provided for a camera or imaging sensor, such as the optical imaging sensor 122 of the microscope 120. The microscope 120 may further include one or more optical magnification components, such as an objective lens (i.e., a lens), used to magnify the scene on the sample.

[0026] There are various types of microscopes. When microscopes are used in the medical or biological fields, the object 10 observed through the microscope may be, for example, a sample of organic tissue placed in a petri dish or present in a part of a patient's body. In the present disclosure, the microscope 120 is a microscope of a surgical microscope system, i.e., a microscope to be used during a surgical procedure, such as a cancer surgery or tumor surgery. Thus, the object observed through the microscope and shown in the image data may be a sample of the patient's organic tissue, and in particular, a surgical site where a surgeon operates during a surgical procedure.

[0027] 1b is a schematic diagram illustrating an example of a surgical microscope system 100 including a microscope 120 and a system 110. Generally, a (surgical) microscope system is a system including a microscope 120 and additional components that operate in conjunction with the microscope. In other words, a microscope system is a system including a microscope and one or more additional components, such as system 110 (which is a computer system adapted to control, e.g., process, the microscope's imaging sensor data), an illumination system (which is used to illuminate the object being imaged by the microscope), additional sensors, a display, etc.

[0028] lb includes a number of optional components, such as a base unit 105 with a (rotating) stand (which contains system 110), an eyepiece display 150a disposed on microscope 120, an auxiliary display 150b disposed on the base unit, a depth sensor 160, and an arm 170 (robotic or manual) that holds microscope 120 in place and is connected to base unit 105 and microscope 120. Generally, these optional and non-optional components may be connected to system 110, which may be configured to control and / or interact with each component.

[0029] The proposed concept is mainly realized by the system 110, which identifies the depth characteristics of the surgical site and adjusts the iris diaphragm 124 of the microscope 120 according to the depth characteristics. Next, we introduce the basic relationship between the numerical aperture and the depth characteristics.

[0030] The resolution of a microscope at the focal plane of the microscope objective is limited by the diffraction of light, which is determined by the numerical aperture of the microscope. In particular, the maximum resolving power, and therefore the resolution, is proportional to the numerical aperture. In practice, higher resolution is related to a higher numerical aperture, which can be achieved by increasing the size of the iris diaphragm opening.

[0031] However, the numerical aperture has a significant effect on the microscope's depth of field, i.e., the sharpness of the portion of the imaged object that lies outside the focal plane. A large numerical aperture results in a shallow depth of field, so that more of the object appears out of focus due to variations in the distance between the imaged object and the microscope's objective and sensor.

[0032] In microscopy, both high resolution and increased depth of field are desirable characteristics, so a trade-off is established: the numerical aperture is adjusted based on the depth characteristics of the surgical site.

[0033] In this context, the depth characteristics (or depth profile) of the surgical site may be related to or based on the distance between a point on the surface of the surgical site and the microscope. For example, the depth characteristics may include a three-dimensional representation (e.g., a three-dimensional model) of the surface of the surgical site (e.g., based on the distance between the surgical site and the microscope). In some examples, the depth characteristics may include information regarding the maximum and minimum distances between a point located on at least a portion of the surgical site and the microscope.

[0034] In some instances, the actual distance may be less important and not (directly) included in the depth characteristic. In the present concept, the depth characteristic characterizes the depth profile of the surgical site. A relevant aspect of the depth profile is the effect of the clarity of the scene on the surgical site for a given numerical aperture and focal length or working distance. Thus, the depth characteristic may be defined in terms of the percentage of at least a portion of the surgical site that appears to be in focus (around the focal plane), i.e., that appears (sufficiently) sharp.

[0035] The depth characteristic of the depth characteristic also relates to the depth of field of at least a portion of the surgical site, i.e., the depth of field required for the entire portion of the surgical site (or at least a portion thereof, e.g., at least 80%, at least 90%, or at least 95%) to be clearly visible. Thus, by determining the depth characteristic, the depth of field of at least a portion of the surgical site may be determined. The system may be configured to determine the depth of field 130 of at least a portion of the surgical site and adjust the numerical aperture of the microscope based on this depth of field. In this context, the depth of field of at least a portion of the surgical site may be defined by a minimum and a maximum distance of points within at least a portion of the surgical site from the microscope's (objective lens). The system may be configured to adjust the numerical aperture so that the depth of field provided by the microscope matches the depth of field of at least a portion of the surgical site. For example, the system may be configured to set the focal plane of the microscope (e.g., by changing the working distance or by performing a focusing operation) at the center of the depth of field of at least a portion of the surgical site and to select the numerical aperture of the microscope so that the depth of field of the microscope matches the depth of field of at least a portion of the surgical site.

[0036] Because surgeons often spend hours continuously with a surgical microscope, which is their primary means of inspecting a surgical site, preferences arise regarding the optical characteristics of the surgeon's view. For example, some surgeons prefer resolution over depth of field, maintaining a shallow depth of field substantially near the major surfaces of the surgical site and ignoring less sharp portions of the surgical site that are not directly related to the surgical procedure being performed. Other surgeons prefer to see all (or nearly all) of the surgical site clearly, for example, so that they can notice events outside of a small area during surgery. Thus, the system may be configured to adjust the numerical aperture so that the depth of field provided by the microscope is suited to the depth of field and personal preferences of the surgeon using the surgical microscope system. For example, information about a surgeon's preferences, i.e., whether they prefer resolution over depth of field or vice versa, may be stored in the system's storage device, and the system may be configured to select a numerical aperture so that the depth of field is appropriate for the surgeon's personal preferred view, for example, by increasing the numerical aperture relative to a (neutral) numerical aperture identified regardless of the surgeon's personal preference if the surgeon's personal preference indicates that the surgeon prioritizes high resolution over increased depth of field, or by decreasing the numerical aperture relative to the neutral numerical aperture if the surgeon's personal preference indicates that the surgeon prefers increased depth of field over increased resolution. For example, the system may be configured to identify the surgeon's personal preference, for example, by recording adjustments made by the surgeon from the neutral numerical aperture. In some examples, a machine learning model may be trained to identify a numerical aperture preferred by the surgeon from the neutral numerical aperture based on the identified neutral numerical aperture (as a training input sample) and the numerical aperture selected by the surgeon using a supervised learning algorithm (as a desired output). The system may be configured to use the machine learning model described above to identify a numerical aperture that suits the surgeon's personal preferences based on the neutral numerical aperture.

[0037] The proposed concept is based on determining depth characteristics of a surgical site 10 imaged using a microscope. For example, as shown in FIG. 1b, in some examples, a dedicated depth sensor, such as a time-of-flight sensor or a structured optical sensor, may be used to determine depth characteristics of the surgical site, for example, by determining a distance between a point on the surface of the surgical site and the microscope. In other words, a system may be configured to obtain (depth) sensor data from a depth sensor 160 of a surgical microscope system and determine depth characteristics of at least a portion of the surgical site based on the sensor data of the depth sensor. The system may be configured to determine a distance between a point on the surface of the surgical site and the microscope based on the (depth) sensor data of the depth sensor 160.

[0038] Alternatively, the microscope's built-in optical imaging sensor may be used to determine the depth characteristics of the surgical site. Accordingly, the system may be configured to acquire imaging sensor data from the microscope's optical imaging sensor 122 and determine the depth characteristics of at least a portion of the surgical site based on the imaging sensor data. Two approaches for determining a depth profile based on the microscope's optical imaging sensor are presented below.

[0039] In a first approach, stereo photogrammetry may be used to determine a three-dimensional scan of the surgical site. Often, the microscope in a surgical microscope system is a stereo microscope, using a pair of optical imaging sensors (one for each eyepiece display) to image the surgical site. The pair of optical imaging sensors may be used to acquire two sets of imaging sensor data, and the pair of optical imaging sensors may be used to perform stereo photogrammetry on the two sets of imaging sensor data to determine a three-dimensional scan of the surgical site. The system may be configured to determine depth characteristics of the surgical site from the three-dimensional scan of the surgical site.

[0040] In a second approach, image sequences may be captured at different numerical aperture settings (and different focal lengths or working distances), and the clarity of the images may be compared to identify depth characteristics of the surgical site. In other words, the system may be configured to sweep the numerical aperture of the microscope to generate multiple frames of imaging sensor data that are based on different numerical apertures, and to identify depth characteristics of at least a portion of the surgical site based on the multiple frames of imaging sensor data that are based on the different numerical apertures. In other words, the system may be configured to set a sequence of different numerical apertures (thereby sweeping the numerical aperture) and acquire separate image frames of the surgical site for each different numerical aperture setting (i.e., multiple frames of imaging sensor data). In this case, the system may be configured to identify depth characteristics of at least a portion of the surgical site by comparing the clarity of the different image frames of the multiple frames of imaging sensor data.

[0041] The sharpness of the different image frames may be determined based on the contrast of each image and / or the proportion of high spatial frequencies in each image. For example, the system may be configured to determine a depth characteristic of at least a portion of the surgical site based on the contrast and / or the occurrence of spatial frequencies above a predetermined spatial frequency threshold in each frame of the plurality of frames.

[0042] For example, the system may be configured to determine the contrast of each image, e.g., by determining the ratio of the standard deviation to the mean value of the pixels in the image, or by performing a kernel-based comparison between each pixel and its neighboring pixels (i.e., adjacent pixels). The more sharp the image, the higher the contrast of the image generally.

[0043] The system may also be configured to determine the distribution of spatial frequencies in each image, for example, by performing a 2D Fourier transform of the image. The higher the proportion of high spatial frequencies, the more visible fine grain structure in the image, which is the case when portions of the image containing fine grain structure are clearly discerned in the image. FIG. 1c is a graph illustrating an example of the distribution of spatial frequencies in imaging sensor data. Graph 180 shows the distribution of spatial frequencies (x-axis represents spatial frequency, y-axis represents the amount of each spatial frequency in the image), with portion 182 showing the portion of the distribution of spatial frequencies above a predetermined frequency threshold. By determining the integral of the portion of the distribution of spatial frequencies above a predetermined frequency, a quantitative measurement can be determined that can be used to compare the occurrence of fine grain structure in different images.

[0044] Generally, even more important than a proper iris diaphragm setting is that the surgical site be in focus, i.e., that the focal plane be at the surgical site. Because surgical sites tend to have a depth profile (especially when a surgeon is operating within a wound area or cavity), the process of adjusting the numerical aperture may include finding a suitable starting point, i.e., a suitable focal length or working distance. Accordingly, the system may be configured to control a microscope or surgical microscope system to sweep the working distance and / or focal length of the microscope to generate a plurality of separate frames of imaging sensor data that are based on different working distances or focal lengths, and to identify depth characteristics of at least a portion of the surgical site based on the plurality of separate frames of imaging sensor data that are based on different working distances or focal lengths. In this disclosure, the terms "working distance" and "focal length" are used partially interchangeably because, in surgical microscopy, focal length can be adjusted by changing the working distance, i.e., by moving the microscope closer to or further away from the surgical site. However, both the working distance and focal length may be controlled independently.

[0045] FIGS. 1d and 1e are graphs illustrating an example grid of working distance / focal length and numerical aperture setting combinations, highlighting the proposed approach according to one example. In FIGS. 1d and 1e, the x-axis represents working distance or focal length, and the y-axis represents numerical aperture. The 10×10 grid is defined by 10 different numerical aperture settings and 10 different working distance / focal lengths. However, other numbers of working distances / focal lengths and aperture settings can also be used. For example, as shown in FIG. 1d, the system may be configured to identify additional frames of imaging sensor data generated during a working distance or focal length sweep based on a default numerical aperture setting (as indicated by grid cell 184, which represents 10 separate image frames captured with the same numerical aperture setting but different working distances / focal lengths). As shown in FIG. 1e, the system may be configured to select a working distance or focal length based on another plurality of frames of imaging sensor data generated during the sweep of the working distance or focal length (e.g., by comparing the sharpness, i.e., contrast and / or occurrence of spatial frequencies above a predetermined spatial frequency threshold, of the another plurality of image frames, and, e.g., by selecting the working distance / focal length that produces the highest contrast or the highest occurrence of spatial frequencies above a predetermined spatial frequency threshold). The system may be configured to sweep the numerical aperture of the microscope while utilizing the selected working distance or focal length for the generation of a plurality of frames of imaging sensor data that are based on different numerical apertures (as shown by grid cell 186). The system may be configured to select a combination of working distance / focal length and numerical aperture by comparing the multiple image frames (again, by comparing the sharpness, i.e., contrast and / or occurrence of spatial frequencies above a predetermined spatial frequency threshold, of the another plurality of image frames, and, e.g., by selecting the working distance / focal length that produces the highest contrast or the highest occurrence of spatial frequencies above a predetermined spatial frequency threshold).In FIG. 1e, cell 188 is selected, representing a combination of working distance / focal length and numerical aperture.

[0046] 1d and 1e illustrate an approach that (eventually) performs a global sweep across the entire range of numerical apertures and the entire range of working distances / focal lengths. In other words, the system may be configured to perform a numerical aperture sweep across a predetermined (global) range of numerical apertures supported by the microscope. Similarly, the system may be configured to perform a working distance or focal length sweep across a predetermined (global) range of working distances or focal lengths supported by the microscope.

[0047] In some examples, another approach may be used, in which the sweep begins from a starting point (e.g., the currently used numerical aperture and / or working distance / focal length) and continues for as long as the resulting image sharpness (as evidenced by contrast or high spatial frequency) improves (i.e., a heuristic approach). For example, the system may be configured to identify a starting working distance or focal length, for example, by using the microscope's autofocus function or by using the currently used working distance or focal length. Starting from the starting working distance or focal length, the system may be configured to sweep the working distance / focal length for as long as the resulting image frame sharpness improves (i.e., until a local maximum in image sharpness is identified). Additionally or alternatively, the system may be configured to identify a starting numerical aperture, for example, a default numerical aperture, or by using the currently used numerical aperture. Starting from the starting numerical aperture, the system may be configured to sweep the numerical aperture for as long as the resulting image frame sharpness improves (i.e., until a local maximum in image sharpness is identified). For example, a numerical aperture sweep may be performed based on the working distances / focal lengths identified in a working distance / focal length sweep.

[0048] In some examples, multiple local maxima may be identified, for example, by starting from a starting working distance / focal length or starting numerical aperture and sweeping the working distance / focal length or numerical aperture in two directions (i.e., shorter and longer distances, smaller and larger numerical apertures). In other words, each sweep may be performed in two directions, for example, by increasing or decreasing the working distance / focal length or by increasing or decreasing the numerical aperture. For example, if the surgical site includes a deep cavity with a protrusion at its bottom, working distance / focal length and / or iris diaphragm settings may be used to improve overall image clarity or to improve overall image clarity in the center or region of interest of the image.

[0049] Generally, the numerical aperture may be adjusted continuously. In other words, the system may be configured to repeat the determination of depth characteristics and the adjustment of the numerical aperture, for example, periodically, after the working distance of the microscope is changed or after the surgical site is changed (such as when the surgeon removes some tissue). In this case, it may be assumed that the currently used settings are at or near a local maximum. For example, when the depth characteristics are re-determined and the numerical aperture is readjusted, the currently used working distance / focal length and / or numerical aperture may be used as the starting working distance / focal length and starting numerical aperture, respectively. For example, first, a complete sweep may be performed over a predetermined range of working distance / focal length and / or numerical aperture. If the numerical aperture is to be updated, each sweep or multiple sweeps may be performed from the starting working distance / focal length and starting numerical aperture, respectively.

[0050] While the field of view of a surgical microscope is often closely aligned with the surgical site (thus allowing the surgeon to see great detail of the surgical site), generally, some less relevant peripheral area may still be visible in the field of view, for example, allowing the surgeon to observe events outside the immediate area where the surgeon is operating, such as bleeding. However, such events may not need to be shown with the highest clarity, as they may generally be perceived even if that portion of the image is slightly less clear. Therefore, the proposed concept may be applied only to a portion of the field of view (i.e., a portion of the surgical site) that is of actual or increasing interest to the surgeon. Therefore, the system may be configured to identify a region of interest 140 within the surgical site and adjust the numerical aperture of the microscope based on the depth characteristics of the region of interest within the surgical site. In other words, the proposed concept may be applied to the region of interest, thereby reducing its relevance to portions of the surgical site / field of view outside the region of interest.

[0051] Generally, the region of interest may be defined manually by the surgeon (or assistant) or derived from an optical imaging sensor. The system is configured to identify the region of interest based on a user input signal obtained through a user interface of the surgical microscope system. For example, the user interface may be a touchscreen of the surgical microscope system. The surgeon or assistant may mark the region of interest via the touchscreen, and the system may be configured to track the location of the region of interest across the image frame.

[0052] Alternatively, the region of interest may be automatically identified. For example, as shown in FIGS. 1f and 1g, the center of the field of view can be considered to be the region of interest 140, followed by a medium region of interest 190 and a region of non-interest 192. FIG. 1f is a graph showing a depth profile of the surgical site 10 (the x-axis represents the lateral dimension and the y-axis represents the vertical dimension of the depth profile). In the center of the depth profile, the region of interest 140 is shown. Next to the region of interest, the medium region of interest 190 is shown, which is followed by a region of non-interest 192. FIG. 1g is a top view of the same surgical site, where the region of interest 140 is surrounded by the medium region of interest 190 and the region of non-interest 192. The system may be configured to use a weighting function to take various regions into account in comparing the clarity of each image frame, for example by giving regions of interest a higher weight than those of moderate interest or generally the rest of the field of view / surgical site.

[0053] Another approach is to analyze the content of the imaging sensor data. For example, a system may be configured to identify a region of interest based on imaging sensor data from an optical imaging sensor of a microscope, e.g., by identifying a region of interest within the imaging sensor data. The system may be configured to perform image processing on the imaging sensor data to identify a portion of the surgical site being operated on and identify the region of interest based on the portion of the surgical site being operated on. For example, the system may be configured to identify the location of one or more surgical tools in the imaging sensor data and identify the portion of the surgical site being operated on based on the location of the surgical tools. For example, the system may be configured to use a trained machine learning model to identify the portion of the surgical site being operated on, e.g., to identify the location of one or more surgical tools within the imaging sensor data. For example, the trained machine learning model may be trained based on annotated image data, e.g., where the image data is used as a training sample and the location of the surgical site and / or one or more surgical tools is used as a desired output in supervised learning-based training of the machine learning model. Alternatively, the machine learning model may be trained to detect one or more anatomical features, such as a tumor being operated on, e.g., using object detection. The system may be configured to identify the region of interest based on the detected one or more anatomical features.

[0054] Once the depth characteristic is identified, the depth characteristic is used to adjust the numerical aperture. This may be done by setting an appropriate numerical aperture in light of the depth characteristic. For example, a lookup table or function may be used to derive the numerical aperture for a given depth characteristic. If the depth characteristic is identified by sweeping the working distance / focal length and numerical aperture, the working distance / focal length and numerical aperture setting that produces the best sharpness may be used. Alternatively, the lowest numerical aperture setting (i.e., smallest aperture) may be used, which provides a (numerical) improvement over the next higher numerical aperture setting (i.e., larger numerical aperture) that is greater than a (percentage) threshold. In other words, if the (numerical) improvement between one numerical aperture setting and the next higher numerical aperture setting is less than a threshold, the improvement may be considered too small (taking into account the loss of resolution), and the next higher numerical aperture may be used. Finally, a numerical aperture may be selected based on whether the improvement the numerical aperture provides to sharpness (relative to adjacent numerical apertures, e.g., the next higher numerical aperture) is greater than a predetermined threshold.

[0055] In the proposed surgical microscope system, an optical imaging sensor is used to provide imaging sensor data. Accordingly, the optical imaging sensor is configured to generate imaging sensor data. For example, the optical imaging sensor 122 of the microscope 120 may include or be an active pixel sensor (APS)-based imaging sensor or a charge-coupled device (CCD)-based imaging sensor. For example, in an APS-based imaging sensor, light is recorded at each pixel using a pixel photodetector and an active amplifier. APS-based imaging sensors are often based on complementary metal-oxide-semiconductor (CMOS) technology or scientific CMOS (S-CMOS) technology. In a CCD-based imaging sensor, incident photons are converted into electronic charges at a semiconductor-oxide interface, which are then transferred between capacitive bins within the imaging sensor by the imaging sensor's circuitry for imaging. The processing system 110 may be configured to acquire (i.e., receive or read) the imaging sensor data from the optical imaging sensor. The imaging sensor data may be obtained by receiving the imaging sensor data from the optical imaging sensor (e.g., via interface 112), by reading the imaging sensor data from the memory of the optical imaging sensor (e.g., via interface 112), or by reading the imaging sensor data from storage device 116 of system 110, for example, after the imaging sensor data has been written to storage device 116 by the optical imaging sensor or another system or processor.

[0056] The one or more interfaces 112 of the system 110 can correspond to one or more inputs and / or outputs for receiving and / or transmitting information, which may be digital (bit) values ​​according to a specified code, within a module, between modules, or between modules of different entities. For example, the one or more interfaces 112 may include interface circuitry configured to receive and / or transmit information. The one or more processors 114 of the system 110 may be implemented using one or more processing units, one or more processing devices, any means for processing, such as a processor, a computer, or a programmable hardware component operable with appropriately adapted software. In other words, the described functions of the one or more processors 114 may be implemented in software, where the software is executed in one or more programmable hardware components. Such hardware components may include a general-purpose processor, a digital signal processor (DSP), a microcontroller, etc. The one or more storage devices 116 of the system 110 may include at least one element of a group of computer-readable storage media, such as magnetic or optical storage media, e.g., hard disk drives, flash memory, floppy disks, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electronically erasable programmable read-only memory (EEPROM), or network storage.

[0057] Further details and aspects of the system and surgical microscope system are mentioned in connection with the proposed concept or one or more examples described above or below (e.g., FIGS. 2-4). The system and surgical microscope system may include one or more additional optional features, which correspond to one or more aspects of the proposed concept or one or more examples described above or below.

[0058] 2 is a flow chart illustrating an example method for a microscope of a surgical microscope system. The method includes identifying 210 a depth characteristic of a surgical site to be imaged using the microscope. The method includes adjusting 220 a numerical aperture of the microscope based on the depth characteristic of at least a portion of the surgical site.

[0059] For example, the method may be implemented by the system and / or surgical microscope system presented in connection with one of Figures 1a-1g, and features presented in connection with the system or surgical microscope system of Figures 1a-1g may likewise be included in the corresponding method.

[0060] Further details and aspects of the method are mentioned in relation to the proposed concept or one or more examples described above or below (e.g., FIGS. 1a-1g, 3-4). The method may include one or more additional optional features, corresponding to one or more aspects of the proposed concept or one or more examples described above or below.

[0061] Various examples of the present disclosure relate to concepts for automatic iris diaphragm adjustment (or automatic iris diaphragm adjustment).

[0062] The proposed concept is based on automatically adjusting the iris diaphragm based on predetermined criteria. These criteria may include the need for depth of field (or depth of focus). The need for depth of field or depth of focus can be estimated from the sharpness of the image. For example, the distance of different tissue regions from the camera may be measured. If the tissue is flat, increased depth of field is not required, and the iris diaphragm can be opened to increase or optimize resolution. For example, fine-tuning of the focus and / or iris diaphragm may be used to estimate the optimal spot. This results in better depth perception when entering the cavity without changing any settings. For example, the iris diaphragm may be automatically closed when entering the cavity, thereby providing the surgeon with better depth perception.

[0063] FIG. 3 is a schematic diagram illustrating the effect of an iris diaphragm aperture on the depth of field of a microscope scene. Similar to FIG. 1a, FIG. 3 shows a microscope optical imaging sensor 122 used to record light arriving from a surgical site 10, where the light passes through an iris diaphragm 124. An automatic iris diaphragm adjustment system 110 (e.g., the system 110 introduced in connection with FIGS. 1a-1g) is used to automatically adjust the iris diaphragm 124 between a first setting 310 having a smaller aperture and a second setting 320 having a larger aperture. The first setting 310 reduces resolution while increasing depth of field 130, while the second setting 320 improves resolution while reducing depth of field 130. The illustrated settings represent the extremes. Settings between these two settings are similarly possible, establishing a tradeoff between resolution and depth of field.

[0064] In some examples, the process may be applied only to regions of interest, which may be defined by a user or automatically identified.

[0065] Estimating the depth of field required is directly related to the tissue "heterogeneity," which can be represented by the depth characteristics of the surgical site. Heterogeneity or depth characteristics can be estimated using various methods, such as rapid sequential capture of images with different iris diaphragm settings and comparing image sharpness, or performing a 3D scan of the surgical cavity, e.g., stereo photogrammetry.

[0066] In some examples, machine learning can be used for various purposes, for example, to identify regions of interest or to adjust the desired depth of field to the personal preferences of a particular surgeon.

[0067] Further details and aspects of the proposed concept for automatic iris diaphragm adjustment are mentioned in connection with the proposed concept or one or more examples described above or below (e.g., FIGS. 1a-2, 4). The proposed concept for automatic iris diaphragm adjustment may include one or more additional optional features, which correspond to one or more aspects of the proposed concept or one or more examples described above or below.

[0068] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as " / ".

[0069] While some aspects have been described in the context of an apparatus, it will be apparent that these aspects also represent a description of a corresponding method, where a block or apparatus corresponds to a step or feature of a step, and similarly, aspects described in the context of a step also represent a description of a corresponding block or item or feature of a corresponding apparatus.

[0070] Some embodiments relate to a microscope including a system such as that described in connection with one or more of FIGS. 1-3. Alternatively, the microscope may be part of a system such as that described in connection with one or more of FIGS. 1-3 or may be connected to a system such as that described in connection with one or more of FIGS. 1-3. FIG. 4 shows a schematic diagram of a system 400 configured to perform the methods described herein. The system 400 includes a microscope 410 and a computer system 420. The microscope 410 is configured to capture images and is connected to the computer system 420. The computer system 420 is configured to perform at least a portion of the methods described herein. The computer system 420 may be configured to execute a machine learning algorithm. The computer system 420 and the microscope 410 may be separate entities or may be integrated into a common housing. The computer system 420 may be part of the central processing system of the microscope 410 and / or the computer system 420 may be part of a subordinate component of the microscope 410, such as a sensor, operating unit, camera or lighting unit of the microscope 410.

[0071] The computer system 420 may be a local computing device (e.g., a personal computer, laptop, tablet computer, or mobile phone) having one or more processors and one or more storage devices, or may be a distributed computing system (such as a cloud computing system having one or more processors and one or more storage devices distributed across various locations, e.g., local clients and / or one or more remote server farms and / or data centers). The computer system 420 may include any circuit or combination of circuits. In one embodiment, the computer system 420 may include one or more processors, which may be of any type. As used herein, a processor may refer to any type of computing circuit, 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 gate array (FPGA), etc., of a microscope or microscope component (e.g., a camera) or any other type of processor or processing circuit. Other types of circuits that may be included in computer system 420 may be custom circuits, application specific integrated circuits (ASICs), etc., and may be one or more circuits (such as communications circuits) used in wireless devices such as mobile phones, tablet computers, laptop computers, two-way radios, and similar electronic systems.Computer system 420 may include one or more storage devices, which may include one or more memory elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard drives, and / or one or more drives that handle removable media, such as compact discs (CDs), flash memory cards, digital video discs (DVDs), etc. Computer system 420 may also include a display device, one or more speakers, and a keyboard and / or controller, which may include a mouse, trackball, touch screen, voice recognition device, or any other device that allows a system user to input information to and receive information from computer system 420.

[0072] Some or all of the steps may be performed by (or using) a hardware apparatus, such as, for example, a processor, microprocessor, programmable computer, or electronic circuitry. In some embodiments, any one or more of the critical steps may be performed by such an apparatus.

[0073] Depending on certain implementation requirements, embodiments of the present invention can be implemented in hardware or software. This implementation can be performed using a non-transitory storage medium such as a digital storage medium, for example, a floppy disk, DVD, Blu-ray, CD, ROM, PROM, EPROM, EEPROM, or flash memory, on which electronically readable control signals are stored that cooperate (or can cooperate) with a programmable computer system so that the respective methods are performed. Therefore, the digital storage medium can be a computer-readable storage medium.

[0074] Some embodiments of the present invention include a data carrier having electronically readable control signals capable of cooperating with a programmable computer system to perform any of the methods described herein.

[0075] Generally, embodiments of the present invention can be implemented as a computer program product comprising program code that operates to perform any of the methods when the computer program product is run on a computer, and that may be stored, for example, on a machine-readable carrier.

[0076] Further embodiments comprise the computer program for performing any of the methods described herein, stored on a machine readable carrier.

[0077] In other words, an embodiment of the present invention is, therefore, a computer program having a program code for performing any of the methods described herein when the computer program runs on a computer.

[0078] Therefore, another embodiment of the invention is a storage medium (or data carrier, or computer-readable medium) comprising a computer program stored thereon for performing any of the methods described herein when executed by a processor. The data carrier, digital storage medium, or recorded medium is typically tangible and / or non-transitory. A further embodiment of the invention is an apparatus as described herein, comprising a processor and a storage medium.

[0079] A further embodiment of the present invention is, therefore, a data stream or a sequence of signals representing the computer program for performing any of the methods described herein, the data stream or sequence of signals being for example adapted to be transmitted via a data communication connection, for example via the Internet.

[0080] Another embodiment comprises a processing means, for example a computer, or a programmable logic device, configured to or adapted to perform any of the methods described herein.

[0081] Another embodiment comprises a computer having installed thereon the computer program for performing any of the methods described herein.

[0082] Another embodiment according to the invention includes an apparatus or system configured to transfer (e.g., electronically or optically) a computer program for implementing any of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a memory device, etc. The apparatus or system may include, for example, a file server for transferring the computer program to the receiver.

[0083] 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, a field programmable gate array may cooperate with a microprocessor to perform any of the methods described herein. Generally, the methods are preferably performed by any hardware apparatus.

[0084] Embodiments may be based on the use of machine learning models or algorithms. Instead of relying on models and inference, machine learning may refer to algorithms and statistical models that a computer system may use to perform a particular task without using explicit instructions. For example, machine learning may use transformations of data inferred from analysis of past data and / or training data instead of rule-based transformations of data. For example, the content of images may be analyzed using machine learning models or algorithms. For a machine learning model to analyze the content of images, the machine learning model may be trained using training images as input and training content information as output. By training the machine learning model with a large number of training images and / or training sequences (e.g., words or sentences) and associated training content information (e.g., labels or annotations), the machine learning model “learns” to recognize image content and can therefore be used to recognize image content not included in the training data. The same principle may be used for other types of sensor data: by training the machine learning model using training sensor data and a desired output, the machine learning model “learns” a transformation between sensor data and output, which can be used to provide an output based on the non-training sensor data provided to the machine learning model. The provided data (e.g., sensor data, metadata and / or image data) may be pre-processed to obtain feature vectors that are used as input to machine learning models.

[0085] A machine learning model may be trained using training input data. The above example uses a training method called "supervised learning." In supervised learning, a machine learning model is trained using multiple training samples, where each sample may include multiple input data values ​​and multiple desired output values, i.e., each training sample is associated with a desired output value. By specifying both the training samples and the desired output value, the machine learning model "learns" which output value to provide based on input samples similar to the samples provided during training. In addition to supervised learning, semi-supervised learning may be used. In semi-supervised learning, some of the training samples lack a corresponding desired output value. Supervised learning may be based on a supervised learning algorithm (e.g., a classification algorithm, a regression algorithm, or a similarity learning algorithm). When the output is restricted to a limited set of values ​​(categorical variables), i.e., the input is classified into one of a limited set of values, a classification algorithm may be used. When the output can have any numerical value (within a range), a regression algorithm may be used. Similarity learning algorithms may be similar to both classification and regression algorithms, but are based on learning from examples using a similarity function that measures how similar or related two objects are. In addition to supervised or semi-supervised learning, unsupervised learning may be used to train machine learning models. In unsupervised learning, input data may be provided (only), and an unsupervised learning algorithm may be used to find structure in the input data (e.g., by grouping or clustering the input data, finding commonalities in the data). Clustering is the assignment of input data containing multiple input values ​​into subsets (clusters) such that input values ​​within the same cluster are similar according to one or more (predetermined) similarity criteria, but dissimilar to input values ​​contained in other clusters.

[0086] Reinforcement learning is a third group of machine learning algorithms. In other words, reinforcement learning may be used to train machine learning models. In reinforcement learning, one or more software actors (referred to as "software agents") are trained to take actions in an environment. Based on the actions taken, a reward is calculated. Reinforcement learning is based on training one or more software agents to select actions that result in an increasing cumulative reward (as evidenced by an increasing reward), resulting in the software agent becoming better at a given task.

[0087] Furthermore, some techniques may be applied to some machine learning algorithms. For example, feature representation learning may be used. In other words, a machine learning model may be trained at least in part using feature representation learning, and / or a machine learning algorithm may include a feature representation learning component. A feature representation learning algorithm, which may be referred to as a representation learning algorithm, not only preserves the information in its input, but can also transform the information so that it becomes useful, often as a preprocessing step before performing classification or prediction. Feature representation learning may be based on, for example, principal component analysis or cluster analysis.

[0088] In some examples, anomaly detection (i.e., outlier detection) may be used, which aims to provide identification of input values ​​that raise suspicion by differing significantly from the majority of the input or training data. In other words, machine learning models may be trained at least in part using anomaly detection, and / or machine learning algorithms may include anomaly detection components.

[0089] In some examples, a machine learning algorithm may use a decision tree as a predictive model. In other words, the machine learning model may be based on a decision tree. In a decision tree, observations about an item (e.g., a set of input values) may be represented by branches of the decision tree, and output values ​​corresponding to the item may be represented by leaves of the decision tree. The decision tree may support both discrete and continuous values ​​as output values. If discrete values ​​are used, the decision tree may be represented as a classification tree, and if continuous values ​​are used, the decision tree may be represented as a regression tree.

[0090] Association rules are another technique that can be used in machine learning algorithms. In other words, a machine learning model can be based on one or more association rules. Association rules are created by identifying relationships between variables in large amounts of data. A machine learning algorithm can identify and / or utilize one or more relational rules that represent knowledge derived from the data. These rules can be used, for example, to store, manipulate, or apply the knowledge.

[0091] Machine learning algorithms are typically based on machine learning models. In other words, the term "machine learning algorithm" may refer to a set of instructions that can be used to create, train, or use a machine learning model. The term "machine learning model" may refer to a data structure and / or a set of rules that represent learned knowledge (e.g., based on training performed by a machine learning algorithm). In embodiments, use of a machine learning algorithm may refer to use of an underlying machine learning model (or underlying machine learning models). Use of a machine learning model may refer to the machine learning model and / or the set of data structures / rules that are the machine learning model being trained by a machine learning algorithm.

[0092] For example, the machine learning model may be an artificial neural network (ANN). An ANN is a system inspired by biological neural networks such as those found in the retina or brain. An ANN includes multiple interconnected nodes and multiple connections, called edges, between the nodes. Typically, there are three types of nodes: input nodes that receive input values, hidden nodes that are (only) connected to other nodes, and output nodes that provide output values. Each node may represent an artificial neuron. Each edge may transmit information from one node to another. The output of a node may be defined as a (nonlinear) function of its input (e.g., the sum of its inputs). The input of a node may be used in a function based on the "weights" of the edges or nodes that provide the input. The weights of the nodes and / or edges may be adjusted during the learning process. In other words, training an artificial neural network may involve adjusting the weights of the nodes and / or edges of the artificial neural network, for example, to obtain a desired output for a given input.

[0093] Alternatively, the machine learning model may be a support vector machine, a random forest model, or a gradient boosting model. A support vector machine (i.e., a support vector network) is a supervised learning model with an associated learning algorithm that can be used to analyze data (e.g., in classification or regression analysis). A support vector machine may be trained by providing input with multiple training input values ​​that belong to one of two categories. A support vector machine may be trained to assign new input values ​​to one of two categories. Alternatively, the machine learning model may be a Bayesian network, which is a probabilistic directed acyclic graphical model. A Bayesian network may use a directed acyclic graph to represent a set of random variables and their conditional dependencies. Alternatively, the machine learning model may be based on a genetic algorithm, which is a search algorithm and heuristic technique that mimics the process of natural selection. [Explanation of symbols]

[0094] 10 Surgical site 100 Surgical Microscope System 105 Base Unit 110 System 112 One or more interfaces 114 one or more processors 116 One or more storage devices 120 Microscope 122 Optical Imaging Sensor 124 Iris Diaphragm 130 depth of field 140 Areas of Interest 150a eyepiece display 150b Auxiliary Display 160 Depth Sensor 170 Arm 180 Spatial frequency distribution 182 Portion of spatial frequency distribution above threshold 184 Cells used for sweeping working distance / focal length 186 Cells used for sweeping numerical aperture 188 selected cells 190 Medium Areas of Interest 192 Area of ​​Non-Interest 210 Identifying Depth Characteristics 220 Adjusting the aperture 310 First setting with smaller aperture 320 Second setting with larger aperture 400 System 410 Microscope 420 Computer Systems

Claims

1. A system (110; 420) for a microscope (120; 410) of a surgical microscope system (100; 400), comprising: The system (110; 420) includes one or more processors (114) and one or more storage devices (116); The system comprises: Identifying depth characteristics of the surgical site (10) to be imaged using said microscope; adjusting the numerical aperture of the microscope based on the depth characteristic of at least a portion of the surgical site; It is configured as follows: System (110; 420).

2. The system comprises: determining a depth of field (130) of at least the portion of the surgical site; adjusting the numerical aperture of the microscope based on the depth of field; It is configured as follows: The system of claim 1 .

3. the system is configured to adjust the numerical aperture so that the depth of field provided by the microscope matches the depth of field of at least the portion of the surgical site. The system of claim 2.

4. the system is configured to adjust the numerical aperture so that the depth of field provided by the microscope is more suited to a personal depth of field preference of a surgeon using the surgical microscope system. The system of claim 3.

5. The system comprises: Identifying a region of interest (140) within the surgical site; adjusting the numerical aperture of the microscope based on the depth characteristics of the region of interest within the surgical site; It is configured as follows: The system of claim 1 .

6. The system comprises: acquiring imaging sensor data from an optical imaging sensor (122) of the microscope; identifying the region of interest based on the imaging sensor data; It is configured as follows: The system of claim 5.

7. The system comprises: performing image processing on the imaging sensor data to identify the portion of the surgical site where surgery is being performed; identifying the region of interest based on the portion of the surgical site where surgery is being performed; It is configured as follows: The system of claim 6.

8. the system is configured to identify the region of interest based on a user input signal obtained through a user interface of the surgical microscope system. The system of claim 5.

9. The system comprises: acquiring sensor data from a depth sensor (160) of the surgical microscope system; determining the depth characteristic of at least the portion of the surgical site based on the sensor data of the depth sensor; It is configured as follows: The system of claim 1 .

10. The system comprises: acquiring imaging sensor data from an optical imaging sensor (122) of the microscope; determining the depth characteristic of at least the portion of the surgical site based on the imaging sensor data; It is configured as follows: The system of claim 1 .

11. The system comprises: sweeping the numerical aperture of the microscope to generate a plurality of frames of imaging sensor data based on different numerical apertures; determining the depth characteristic of at least the portion of the surgical site based on the plurality of frames of the imaging sensor data based on the different numerical apertures; It is configured as follows: The system of claim 10.

12. the system is configured to identify the depth characteristic of at least the portion of the surgical site based on contrast and / or based on an occurrence of spatial frequencies above a predetermined spatial frequency threshold in each frame of the plurality of frames. The system of claim 11.

13. The system comprises: controlling the microscope or surgical microscope system to perform a sweep of the working distance and / or focal length of the microscope to generate another plurality of frames of imaging sensor data based on different working distances or focal lengths; determining the depth characteristic of at least the portion of the surgical site based on the further plurality of frames of imaging sensor data based on the different working distances or focal lengths; It is configured as follows: The system of claim 11.

14. The system comprises: selecting a working distance or focal length based on a frame of the other plurality of frames of imaging sensor data generated during the sweep of the working distance or focal length; sweeping the numerical aperture of the microscope while using the selected working distance or focal length to generate the plurality of frames of imaging sensor data based on the different numerical apertures; It is configured as follows: The system of claim 13.

15. microscope (120; 410) and The system (110; 420) according to any one of claims 1 to 14, A surgical microscope system (100; 400) including:

16. 1. A method for a microscope of a surgical microscope system, said method comprising: identifying (210) a depth characteristic of the surgical site being imaged using the microscope; adjusting (220) the numerical aperture of the microscope based on the depth characteristics of at least a portion of the surgical site; A method comprising:

17. A computer program comprising: The computer program comprises program code for performing the method of claim 16 when the computer program is run on a processor. Computer program.

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