Ophthalmic microscope system and system, method and computer program for an ophthalmic microscope system

Abstract

Each example relates to a system (110) for an ophthalmic microscope system, a method and a computer program product, and a corresponding ophthalmic microscope system. The system is configured to obtain imaging sensor data from an optical imaging sensor (122) of a microscope of the ophthalmic microscope system. The system is configured to determine information regarding an anatomical feature of the eye represented in the imaging sensor data. The system is configured to control an illumination system (130) of the ophthalmic microscope system to adjust characteristics of a light beam used for red reflex illumination of the eye based on the information regarding the anatomical feature.

Description

[Technical field]

[0001] The examples relate to systems, methods and computer programs for ophthalmic microscope systems and corresponding ophthalmic microscope systems. [Background technology]

[0002] In an ophthalmic surgical microscope, the primary light is used to illuminate the anterior surface of the eye, and the red reflex light is used to illuminate the retina to obtain a bright orange reflection, allowing transparent structures such as the lens capsule to be seen during deformation. Ideally, the red reflex light should provide a sharp, uniform reflection from the retina without interfering with illuminating the anterior surface of the eye.

[0003] In most ophthalmic microscopes, the red reflex covers a static circular area centered on the center of the field of view. A small radius can cause the red reflex to dim or disappear when the eye moves, while a large radius can cause light to spill over into the anterior segment and overbrighten the peripheral areas. The effects of light spill are especially noticeable in digital head-up surgery, where the dynamic range of digital video cameras is narrower than that of a human observer.

[0004] Low red reflex intensity often results in reduced visibility. High red reflex intensity can cause unnecessary damage to the retina. The optimal red reflex intensity setting for each eye may vary and may require manual adjustments during surgery. For certain procedures, such as hydrodissection, the light intensity required to achieve good visualization may also vary significantly.

[0005] In some ophthalmic microscopes, the red reflex intensity can be manually adjusted independently of the main light, and its coverage can be adjusted using an iris diaphragm (also called an iris diaphragm) via a fly-by-wire knob, however this is a manual process and not an automatic process for tracking the eye.

[0006] There is a desire to improve the concept for ophthalmic surgical microscopes. Summary of the Invention [Problem to be solved by the invention]

[0007] This need is solved by the subject matter of the independent claims.

[0008] The concept proposed in this disclosure is based on the finding that the position of the eye during ophthalmic surgery is often not completely stationary, i.e., the eye may move (or be moved) during surgery. Furthermore, the red reflex effect may change during surgery, for example as a result of hydrodissection. Hence, a static red reflex light setting may be insufficient to address such changes. The proposed concept uses image analysis to determine information of anatomical features of the eye, such as the position of the pupil or limbus, or the intensity of the red reflex effect, and uses that information to adjust the light beam used for red reflex illumination, for example by adjusting the path of the light beam and thus the target area of ​​the eye, or by adjusting the intensity of the light beam. Thus, the center and radius of the red reflex illumination iris can be automatically controlled based on the live surgical video. Furthermore, the intensity of the red reflex illumination can be automatically adjusted based on the live surgical video. Various examples of the present disclosure can provide a means for automatic red reflex light pattern and / or intensity control.

[0009] Various examples of the present disclosure relate to a system for an ophthalmic microscope system. The system is used to control various aspects of the ophthalmic microscope system and can be integrated into the ophthalmic microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain imaging sensor data from an optical imaging sensor of a microscope of the ophthalmic microscope system. The system is configured to determine information regarding an anatomical feature of the eye indicated in the imaging sensor data. The system is configured to control an illumination system of the ophthalmic microscope system to adjust characteristics of a light beam used for red reflex illumination of the eye based on the information regarding the anatomical feature. By determining the information regarding the anatomical feature, the system can determine whether the light beam needs to be adjusted following a movement or other change that affects the red reflex and can adjust the characteristics of the light beam accordingly.

[0010] In some examples, the anatomical feature is a pupil of the eye. The system may be configured to determine information about the pupil of the eye and control the illumination system to adjust the characteristics of the light beam based on the information about the pupil of the eye. Alternatively, the anatomical feature may be (or may relate to) the limbus of the eye. The system may be configured to determine information about the limbus of the eye and control the illumination system to adjust the characteristics of the light beam based on the information about the limbus of the eye. The positions of both the pupil and the limbus may be used to determine the direction and / or diameter of the light beam.

[0011] For both the pupil and the limbus, the position (and optionally its size) of each feature is relevant with respect to the light beam. For example, the system may be configured to determine the position of an ocular anatomical feature, such as the pupil or the limbus. The system may be configured to control the illumination system to adjust the path of the light beam based on the position of the ocular anatomical feature. In other words, the target of the light beam can be adjusted depending on the position of the ocular anatomical feature.

[0012] There are various means that can be used to adjust the path of the light beam. For example, the system may be configured to control one or more motors to adjust the position of at least one of a light source and an iris of the illumination system to adjust the path of the light beam. By moving the iris and / or the light source, the path of the light beam can be shifted perpendicular to the direction of the light beam.

[0013] Alternatively (or additionally), the system may be configured to control a display device configured to modulate the light beam to adjust the path of the light beam. For example, the light beam may pass through a portion of the display device (e.g., a first set of pixels), the position and size of which may determine the path (and width) of the light beam. Alternatively, the light beam may be reflected by a portion of the display device (e.g., a first set of pixels), the position and size of which may determine the reflected path (and width) of the light beam.

[0014] As outlined above, the size of the portion of the display device affects the width (e.g., diameter) of the light beam. Alternatively, the width of the light beam may be adjusted by adjusting the iris described above. The system may be configured to determine the size of an anatomical feature of the eye and control the illumination system to adjust the width of the light beam based on the size of the anatomical feature of the eye. For example, the system may be configured to control the iris of the illumination system to adjust the width (or diameter) of the light beam. Alternatively, the system may be configured to control a display device configured to modulate the light beam to adjust the width of the light beam. The width of the light beam may be adjusted to account for different sizes of pupils or to account for changes in the size (or position) of the pupil.

[0015] During ophthalmic surgery, surgical manipulations performed by the surgeon, such as hydrodissection, can cause changes in the red reflex. Therefore, the proposed concept can be used to compensate for such changes. The system may be configured to determine a brightness or contrast of the red reflex illumination of the eye. The system may be configured to control the illumination system to adjust the intensity of the light beam based on the brightness or contrast of the red reflex illumination. For example, the system may be configured to control a light source of the illumination system to adjust the intensity of the light beam. By adjusting the intensity of the light beam, the visibility of the red reflex can be kept at an appropriate level.

[0016] In general, the proposed concept can be used intraoperatively to adjust the light beam to changes occurring during surgery. In other words, the proposed system can react to changes in the anatomical features and adjust the light beam accordingly after (e.g., in response to) the changes. For example, the system can be configured to update information about the anatomical features over a series of frames of imaging sensor data and control the illumination system to adjust characteristics of the light beam upon detection of changes in information about the anatomical features of the eye.

[0017] In various examples, machine learning (i.e., "artificial intelligence") may be employed to determine information regarding the anatomical features. For example, the system may be configured to detect anatomical features in the imaging sensor data using a machine learning model that is trained to detect anatomical features in the imaging sensor data. The system may be configured to determine information regarding the anatomical features based on the output of the machine learning model. For example, machine learning based object detection may be used to track the anatomical features and / or determine the extent (e.g., location and size) of the anatomical features and use the information to adjust the characteristics of the light beam. Thus, the output of the machine learning model may include at least one of information regarding the location and information regarding the contour of the anatomical features. For example, the machine learning model may be one of a convolutional neural network-based object detector and a gradient-based object detector. Both types of detectors are suitable for determining the location and / or contour of the anatomical features.

[0018] In some examples, the illumination can be modulated for purposes of determining information regarding the anatomical feature. For example, the coverage of the light beam can be temporarily increased (e.g., set to maximum) and the resulting difference in red reflex from adjacent regions can be used to determine the position of the pupil. For example, the system can be configured to control the illumination system to temporarily increase the beam width of the light beam and determine information regarding the anatomical feature based on imaging sensor data generated based on the increased beam width. Alternatively or additionally, the system can be configured to control the illumination system to temporarily disable the light beam. The system can be configured to compare imaging sensor data generated while the light beam is disabled with imaging sensor data generated while the light beam is enabled. The system can be configured to determine information regarding the anatomical feature based on the comparison. For example, the pupil can be determined from a region that is significantly brighter when the light beam is enabled.

[0019] Various examples of the present disclosure relate to an ophthalmic (surgical) microscope system that includes the above-mentioned system, a microscope and an illumination system.

[0020] As outlined above, different means can be used to adjust the path of the light beam. For example, the position of the light source and / or the iris can be moved to adjust the path of the light beam. For example, the lighting system can comprise one or more motors for adjusting the position of at least one of the light source and the iris of the lighting system. The system can be configured to control the one or more motors to adjust the position of at least one of the light source and the iris of the lighting system in order to adjust the path of the light beam emitted by the lighting system.

[0021] Alternatively, the light beam may be transmitted through or reflected by a display device. For example, the illumination system may comprise a display device for modulating the light beam emitted by the illumination system. The system may be configured to control the display device to adjust at least one of the path and width of the light beam.

[0022] In some examples, the light beam may be reflected from a portion of a display device. For example, an illumination system may include a light source configured to emit a light beam and a display device. The display device may be configured to selectively reflect the light beam toward the eye, thereby modulating the light beam.

[0023] Alternatively, the light beam may be transmitted through a display device, where again the illumination system may comprise a light source configured to emit a light beam and a display device, which may be configured to modulate the light beam as it passes through the display device towards the eye.

[0024] Various examples of the present disclosure relate to a corresponding method for an ophthalmic microscope system, the method including obtaining imaging sensor data from an optical imaging sensor of a microscope of the ophthalmic microscope system, the method including determining information related to an anatomical feature of an eye indicated in the imaging sensor data, the method including controlling an illumination system of the ophthalmic microscope system to adjust a characteristic of a light beam used for red reflex illumination of the eye based on the information related to the anatomical feature.

[0025] Various examples of the present disclosure relate to corresponding computer programs having program codes for performing the above methods, when the programs are executed in a processor. [Brief description of the drawings]

[0026] Some examples of apparatus and / or methods are now described, by way of example only, and with reference to the accompanying drawings, in which: [Figure 1a] FIG. 1 illustrates a block diagram of an example system for an ophthalmic microscope system. [Figure 1b] FIG. 1 shows a schematic diagram of an example of an ophthalmic microscope system. [Figure 1c] 1A-1C are schematic diagrams of examples of ophthalmic microscope systems, in particular different illumination systems for ophthalmic microscope systems. [Figure 1d] 1A-1C are schematic diagrams of examples of ophthalmic microscope systems, in particular different illumination systems for ophthalmic microscope systems. [Figure 1e] 1A-1C are schematic diagrams of examples of ophthalmic microscope systems, in particular different illumination systems for ophthalmic microscope systems. [Diagram 2] FIG. 1 shows a flowchart of an example method for an ophthalmic microscope system. [Diagram 3] FIG. 1 shows a block diagram of an example of components of the proposed ophthalmic microscope system. [Figure 4] FIG. 2 shows a schematic diagram of an example of a mechanically adjustable iris. [Diagram 5] FIG. 2 shows a schematic diagram of an example of an electronically adjustable iris. [Figure 6] FIG. 1 shows a schematic diagram of a system comprising a microscope and a computer system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Various examples will now be described in more detail with reference to the accompanying drawings, in which several examples are illustrated, in which the thickness of lines, layers and / or regions may be exaggerated for clarity.

[0028] 1a shows a block diagram of an example of a system 110 of an ophthalmic microscope system 100. The system 110 is responsible for controlling various aspects of the microscope 120 of the ophthalmic microscope system and the entire ophthalmic microscope system and / or for processing various types of sensor data of the ophthalmic microscope system. As a result, the system 110 may be implemented as a computer system that interfaces with various components of the ophthalmic microscope system.

[0029] The system 110 comprises one or more processors 114 and one or more storage devices 116, as shown in FIG. 1a. Optionally, the system further comprises one or more interfaces 112. The one or more processors 114 are coupled to the one or more storage devices 116 and the optional one or more interfaces 112. In general, the functionality of the system is provided by the one or more processors 114 in combination with the one or more interfaces 112 (e.g., for exchanging information with at least one optical imaging sensor 122;124 of the microscope 120, the illumination system 130 of the ophthalmic microscope system and / or the display device 140a;140b of the surgical microscope system (shown in FIG. 1b)) and / or the one or more storage devices 116 (for storing and / or retrieving information). The system is configured to obtain imaging sensor data from the at least one optical imaging sensor 122;124 of the microscope 120 of the ophthalmic microscope system. The system is configured to determine information regarding an anatomical feature of the eye represented in the imaging sensor data. The system is configured to control the illumination system 130 of the ophthalmic microscope system to adjust the characteristics of the light beam used for red reflex illumination of the eye based on information about the anatomical features.

[0030] The proposed concept involves three components of an ophthalmic microscope system 100 that interact: a system 110 that processes the microscope imaging sensor data and controls the illumination system; a microscope 120 that is used to generate the imaging sensor data; and an illumination system 130 that is used to provide red reflex illumination.

[0031] FIG. 1b is a schematic diagram of an example of an ophthalmic microscope system 100 comprising a system 110, a microscope 120, and an illumination system (not shown in FIG. 1b). In general, an (ophthalmic) microscope system is a system comprising a microscope 120 and additional components operated together with the microscope. In other words, a microscope system is a system comprising a microscope and one or more additional components, such as a system 110 (which is a computer system adapted to control and, for example, process, the imaging sensor data of the microscope), an illumination system 130 (used to illuminate the object being imaged by the microscope), additional sensors, a display, etc.

[0032] The ophthalmic microscope system (or ophthalmic surgical microscope system) 100 shown in Figure 1b comprises a number of optional components, such as a base unit 105 with a (rolling) stand (which contains the system 110), an eyepiece display 140a located on the microscope 120, an auxiliary display 140b located on the base unit, and an arm 150 (robotic or manual) that holds the microscope 120 in place and is coupled to the base unit 105 and the microscope 120. In general, these optional and non-optional components can be coupled to the system 110, which may be configured to control and / or interact with each of the components.

[0033] The proposed concept is based on analyzing imaging sensor data of at least one optical imaging sensor 122;124 of a microscope 120 of an ophthalmic microscope system. In general, 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 may provide optical magnification of a sample, such as the eyeball 10 shown in Fig. 1b-1e. In modern microscopes, the optical magnification is often provided to a camera or imaging sensor, such as the at least one optical imaging sensor 122;124 of the microscope 120. In particular, the microscope 120 may be a stereo microscope, and the optical magnification may be provided to two separate optical imaging sensors 122;124, as shown in Fig. 1c-1e. The microscope 120 may further comprise one or more optical magnification components, such as objective lenses (i.e. lenses), used to magnify the field of view of the sample.

[0034] There are different types of microscopes. When microscopes are used in the medical or biological fields, the object 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 an ophthalmic microscope system, i.e. a microscope to be used during an ophthalmic surgical procedure, i.e. during eye surgery. Thus, the object observed through the microscope and shown in the image data may be the eye 10 of a patient operated on during a surgical procedure.

[0035] The microscope 120 comprises at least one optical imaging sensor 122;124 configured to provide imaging sensor data processed by the system 100. In other words, the system 110 is configured to perform image processing on the imaging sensor data to determine information about the anatomical features of the eye shown in the imaging sensor data. In the present concept, the anatomical features of the target have at least three aspects: the location of the anatomical feature, the extent (i.e., size) of the anatomical feature, and the effect of the light beam on the anatomical feature (i.e., intensity of the red reflex). Thus, the information about the anatomical feature may include at least one of information about the location of the anatomical feature, information about the contour of the anatomical feature (e.g., information about the width / diameter of the anatomical feature), and information about the intensity of the red reflex observed through the anatomical feature. The present system may be configured to determine at least one of information about the location of the anatomical feature, information about the contour of the anatomical feature, and information about the intensity of the red reflex observed through the anatomical feature by processing the imaging sensor data.

[0036] In ophthalmology, the red reflex occurs when a light beam of red reflex illumination shines through the pupil onto the back of the eye (fundus), causing a bright orange reflex that indicates the pupil is bright orange in the imaging sensor data. To obtain the red reflex, the light beam of red reflex illumination must be transmitted through the pupil, without light spilling into the anterior eye and over-illuminating the area around the pupil. In practice, the position (and contour) of the pupil is important for the purpose of aiming the light beam. Therefore, the anatomical feature may be or include the pupil of the eye, and the system is configured to determine information about the pupil of the eye. However, in some cases, the transition between the pupil and the iris (i.e., the iris) may be difficult to distinguish. Therefore, instead of (or in addition to) the pupil, the limbus of the eye (i.e., the transition between the cornea and the sclera) may be used as a reference. Therefore, the anatomical feature may be or include the limbus of the eye, and the system is configured to determine information about the limbus of the eye. In Figures 1b and 1c the relevant components of the eye are illustrated, with Fig. 1b showing an eye 10 in an auxiliary display 140b with pupil 12 and iris 14 (both covered by the cornea), sclera 16 and limbus 18 between the sclera 16 and the cornea. In Fig. 1c the eye 10 is shown in cross section showing the sclera 16, cornea 17 and limbus 18 between the sclera 16 and the cornea 17. In the imaging sensor data the sclera appears (mostly) light and the cornea appears dark (due to the visible iris) so that the limbus can be clearly distinguished at the light-dark transition.

[0037] Several approaches may be considered for detecting and tracking the pupil (red reflex boundary) or limbus. In some examples, machine learning may be used to detect the pupil or limbus in the imaging sensor data. For example, the system may be configured to detect anatomical features in the imaging sensor data using a machine learning model that is trained to detect anatomical features in the imaging sensor data and determine information about the anatomical features based on the output of the machine learning model.

[0038] Thus, some examples are based on machine learning. 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 the use of explicit instructions. For example, in machine learning, instead of rule-based data transformations, data transformations inferred from analysis of past data and / or training data may be used. For example, image content may be analyzed using a machine learning model or using a machine learning algorithm. For a machine learning model to analyze image content, the machine learning model may be trained with 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, such that image content not included in the training data becomes recognizable using the machine learning model. The same principle may be used for other types of sensor data in a similar manner: by training the machine learning model with 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 a machine learning model.

[0039] In the present context, the machine learning model is trained to detect anatomical features in the imaging sensor data. In this context, the term "detection" may refer not only to the mere presence of the anatomical feature in the imaging sensor data, but also to the location and / or extent of the anatomical feature in the imaging sensor data. In the object detection that the machine learning model is trained to perform, the output of the machine learning model is typically a so-called bounding box, which is a (usually) rectangular box defined by coordinates, such that when the bounding box is overlaid on the imaging sensor data, the bounding box encloses the feature. Such a bounding box is defined by the positions of the vertices of the bounding box, and therefore also provides the position of the anatomical feature and a contour (e.g., a rectangular contour) (around) the anatomical feature. Thus, the output of the machine learning model may include at least one of information about the position of the anatomical feature and information about the contour. In this context, the information about the contour may be a set of coordinates that defines a rectangular bounding box around the anatomical feature, or information about one or more coordinates that define a non-rectangular contour that more closely tracks the boundary of the anatomical feature and information about the shape of the contour.

[0040] In some examples, the machine learning model may be a convolutional neural network-based object detector. For example, a convolutional neural network-based object detector trained on labeled eye images can be used. A convolutional neural network-based object detector can be used to identify the pupil region (or limbus) from live surgical footage. Examples of such detectors include SSD (Single Shot Detector), YOLO (You Only Look Once), and Faster RCNN (Region-Based Convolutional Neural Network). To train such a detector, multiple sample images of the eye and multiple desired output sets (e.g., bounding box coordinates, coordinates and information on the shape of the contour, i.e., "labels" for the labeled eye images) can be used in a supervised learning-based approach.

[0041] Alternatively, the machine learning model may be a gradient-based object detector. In other words, the pupil region (or limbus) may be identified using a gradient-based object detector such as Haar Cascade or HOG (Histogram of Oriented Gradient). Such machine learning models operate by detecting edges in the imaging sensor data, for example by first converting the imaging sensor data into a representation based on the derivative of each pixel (e.g., with respect to neighboring pixels). Again, a supervised learning approach based on labeled eye images may be used to detect anatomical features of interest.

[0042] Alternatively, the pupil region may be located first by increasing the coverage of the red reflex, e.g., by setting the coverage of the red reflex to a maximum and segmenting the orange region that is likely to correspond to the red reflex. In other words, the system may be configured to control the illumination system to temporarily increase the beam width of the light beam (e.g., to a maximum supported beam width) and determine information about the anatomical feature based on the imaging sensor data generated based on the increased beam width. For example, the imaging sensor data may indicate that one or more regions marked with orange may correspond to a red reflex. The system may be configured to segment the imaging sensor data (e.g., using a machine learning model trained for image segmentation) to determine the one or more regions marked with orange. This may result in several candidate regions, which may be filtered based on size and shape priors to isolate the pupil region. In this manner, the system may be configured to filter one or more regions based on their shape and / or size to isolate one region as the pupil. A corneal feature can then be extracted from just outside the pupil and its position tracked as a surrogate for the pupil.

[0043] The red reflex light may also be switched on and images before and after may be compared to identify areas that are significantly brighter. In other words, the system may be configured to control the illumination system to temporarily disable the light beam and compare imaging sensor data generated while the light beam is disabled with imaging sensor data generated while the light beam is enabled to determine information regarding the anatomical feature based on the comparison. For example, the system may be configured to determine one or more regions in the imaging sensor data that appear substantially (e.g., at least 10%, or at least 25%, or at least 50%) brighter in the imaging sensor data generated while the light beam is enabled compared to the imaging sensor data generated while the light beam is disabled. From these regions, the pupil may then be identified based on other image statistics such as size, shape, and brightness. Thus, the system may be configured to detect the pupil among one or more regions based on the size, shape, and / or brightness of the one or more regions.

[0044] Once the information on the anatomical feature is determined, the system controls the illumination system 130 of the ophthalmic microscope system to adjust at least one characteristic of the light beam. As outlined above, there are (at least) three characteristics of the light beam of interest with respect to red reflex illumination: the path of the light beam, the width of the light beam, and the intensity of the light beam. These characteristics can be derived from information on the anatomical feature, in particular information on the location of the anatomical feature, information on the contour of the anatomical feature, and information on the intensity of the red reflex observed through the anatomical feature (i.e. the pupil). For example, the system may be configured to determine the (desired) path of the light beam based on the information on the location of the anatomical feature, for example by selecting a path that intersects with the location of the anatomical feature. The system may be configured to determine the (desired) width of the light beam based on the information on the contour of the anatomical feature, by selecting a width that fits (as far as possible) the contour of the anatomical feature and / or fills the contour. Finally, as will be explained in more detail at a later stage, the system may be configured to determine the (desired) intensity of the light beam based on information on the intensity of the red reflex observed through the pupil.

[0045] In the following, the (desired) path and the (desired) width of the light beam are discussed. In particular, the system may be configured to determine a position of an anatomical feature of the eye (i.e., information about the position of the anatomical feature) and to control the illumination system to adjust the path of the light beam based on the position of the anatomical feature of the eye, e.g., so that the light beam intersects the position of the anatomical feature. Additionally or alternatively, the system may be configured to determine a size of the anatomical feature of the eye (e.g., based on information about the contour of the anatomical feature) and to control the illumination system to adjust the width of the light beam based on the size of the anatomical feature of the eye, e.g., so that the light beam fits and / or fills (as far as possible) the contour of the anatomical feature. In particular, this may apply to the pupil, i.e., the anatomical feature may be the pupil. Thus, the system may be configured to control the illumination system to adjust the properties of the light beam based on information about the pupil of the eye. Alternatively or additionally, the position and / or contour of the corneal limbus may also be used to determine the position of the pupil. In this manner, the system may be configured to control the illumination system to adjust the characteristics of the light beam based on information about the limbus of the eye.

[0046] In Figures 1c to 1e three different examples are shown in which the path and / or width of the light beam can be adjusted. Figures 1c to 1e show schematic diagrams of examples of ophthalmic microscope systems and in particular different illumination systems for ophthalmic microscope systems.

[0047] FIG. 1c shows an example of an ophthalmic microscope system 100 comprising the system 110, two optical imaging sensors 122;124 (stereoscopic configuration), and an illumination system 130 with a light source 132 and an adjustable iris 134. For example, the illumination system 130 may further comprise one or more motors (not shown) for adjusting the position of at least one of the light source 132 and the iris 134 of the illumination system. For example, as shown in FIG. 4, the illumination system may comprise a platform 410 that houses the iris 134 and is movable in the xy direction perpendicular to the light beam. Optionally, the light source may also be moved together with the iris. Alternatively, a setup without an adjustable iris may be used, where the light source is movable in the xy direction via a similar platform and one or more corresponding motors. The system may be configured to control one or more motors to adjust the position of at least one of the light source and the iris of the illumination system in order to adjust the path of the light beam emitted by the illumination system. In particular, the system may be configured to control one or more motors to move the light source and / or iris in an xy direction perpendicular to the light beam (the light beam defines the z direction). By moving the light source and / or iris, the light beam is shifted (i.e., displaced, offset) relative to the eye to a position where it intersects with the position of the pupil.

[0048] In addition to the path of the light beam, the setup of FIG. 1c can be used to adjust the width of the light beam (via iris 134). For example, the system may be configured to control the iris 134 of the illumination system to adjust the width (or diameter) of the light beam. For example, as shown in FIG. 1c, the light beam may narrow towards the eye (due to the optics of the objective lens). The iris 134 may be controlled such that the width of the light beam when it intersects with the eye corresponds to the above-mentioned desired width of the light beam.

[0049] In Fig. 1d and Fig. 1e, a different concept is used. In Fig. 1d and Fig. 1e, a display device is used to modulate the light beam (i.e., to change the characteristics such as the path and width of the light beam). Fig. 1d and Fig. 1e show an example of an ophthalmic microscope system 100 comprising a system 110, two optical imaging sensors 122; 124 (stereoscopic configuration), and an illumination system 130 with a light source 132 and a display device 136 configured to modulate the light beam. In other words, the illumination system 130 can comprise a display device 136 for modulating the light beam emitted by the illumination system. The system may be configured to control the display device to adjust at least one of the path and width of the light beam, as shown below.

[0050] Figure 1d and Figure 1e show two different examples, differing mainly in the arrangement of the display device and thus the concept used to modulate the light beam. In Figure 1d, the light beam passes through the (at least partially transparent) display device. In this case, the display device 136 is configured to modulate the light beam as it passes through the display device towards the eye. For example, in this configuration, the display device may be a liquid crystal display (LCD) without a backlight. In an LCD, two polarizing filter layers (with mutually orthogonal polarizations) are combined with a layer of liquid crystals, which, when "on" via one or more transistors, can modify the polarization of the light as it passes through the layer of liquid crystals. In the "off state", the polarizations of the polarizing filters are orthogonal to each other and block (or at least attenuate) the light from passing through the LCD. When the liquid crystals of a pixel of the LCD are set to the "on state" (which may vary in intensity), the polarization of the light passing through said pixel is changed, allowing the light to pass through the LCD. In practice, an LCD allows the definition of pixels that block (or at least significantly attenuate) the light and pixels that allow the light to pass through. The system may be configured to control an (LCD) display device such that a light beam path and / or light beam width is modulated (i.e., changed) to a desired light beam path and / or width by setting a first set of pixels to block light (by setting an appropriate first set of pixels to an "on state" such that a light beam passes through the first set of pixels) and setting a second set of pixels to pass light.

[0051] On the other hand, in Fig. 1e, the light beam is reflected by the display device. In other words, the display device 136 may be configured to selectively reflect the light beam toward the eye, thereby modulating the light beam. In this case, similar to the configuration shown in Fig. 1d, a first set of pixels of the display device is set to reflect the light beam, and a second set of pixels of the display device is set to absorb the light (or at least reflect it less intensely). This can be achieved by setting the first set of pixels to white and the second set of pixels to black (if the display device is a self-emissive display panel, such as a backlit LCD or organic light-emitting diode (OLED) display panel), or by setting the first set of pixels to reflect light toward the eye and the second set of pixels to reflect light elsewhere or to absorb light (if the display device is a micromirror array panel).

[0052] As outlined above, in some examples, the intensity of the light beam may be modulated, for example, so that the red reflex appears with a desired brightness or contrast. The system may be configured to determine, for example, from imaging sensor data, the brightness or contrast of the red reflex illumination of the eye (e.g., information regarding the intensity of the red reflex observed through an anatomical feature), and control the illumination system to adjust the intensity of the light beam based on the brightness or contrast of the red reflex illumination. In particular, the system may be configured to adjust the intensity of the light beam such that the brightness or contrast of the red reflex matches the desired brightness or contrast, for example, by iteratively increasing or decreasing the intensity of the light beam. Alternatively, a look-up table may be used to derive the intensity of the light beam from the perceived brightness or contrast of the red reflex. To adjust the intensity of the light beam, the system may control a light source to adjust the intensity of the light emitted by the light source or control a display device to adjust the intensity of the reflection and / or attenuation caused by the display device. In other words, the system may be configured to control the light source 132 of the illumination system to adjust the intensity of the light beam. Alternatively, the system may be configured to control the display device 136 to adjust the intensity of the light beam.

[0053] In addition to the light source 132 shown in Figures 1c-1e, the illumination system may further comprise a separate light source that is used as a primary light for illuminating the eye during surgery. For example, the separate light source may be adjustable (by the system) separately from the light source 132 used for red reflex illumination.

[0054] In general, the proposed concept can be used intraoperatively to adjust the light beam to changes occurring during surgery. In other words, the proposed system can react to changes in the anatomical features and adjust the light beam accordingly after (e.g., in response to) the changes. Thus, the system can be configured to update information about the anatomical features over a series of frames of imaging sensor data and control the illumination system to adjust the characteristics of the light beam upon detection of a change in the information about the anatomical features of the eye. For example, the system can be configured to re-determine information about the anatomical features based on at least a subset of the frames of imaging sensor data, e.g., every nth frame (n>1). If the information about the anatomical features changes between the frames being processed, the characteristics of the light beam can be adjusted accordingly, e.g., if the change between two sets of information about the anatomical features exceeds a predefined threshold, e.g., a predefined distance threshold (for the distance between two positions or contours) or a predefined brightness difference threshold (for the difference between two brightness levels).

[0055] In the proposed ophthalmic microscope system, at least one optical imaging sensor is used to provide imaging sensor data. Thus, the at least one optical imaging sensor is configured to generate imaging sensor data. For example, the at least one optical imaging sensor 122;124 of the microscope 120 may be or include an APS (Active Pixel Sensor) or CCD (Charge-Coupled-Device) based imaging sensor. For example, in an APS-based imaging sensor, light is recorded at each pixel using a photodetector and an active amplifier in the pixel. APS-based imaging sensors are often based on CMOS (Complementary Metal Oxide Semiconductor) or SCMOS (Scientific 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 in the imaging sensor by the imaging sensor's circuitry to perform imaging. The system 110 may be configured to acquire (i.e., receive or read) imaging sensor data from the at least one optical imaging sensor. The imaging sensor data may be obtained by receiving imaging sensor data from the at least one optical imaging sensor (e.g., via interface 112), by reading the imaging sensor data from the memory of the at least one optical imaging sensor (e.g., via interface 112), or by reading the imaging sensor data from the storage device 116 of system 110, for example, after the imaging sensor data has been written to the storage device 116 by the at least one optical imaging sensor or by another system or processor.

[0056] The one or more interfaces 112 of the system 110 may 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 comprise an interface circuit configured to receive and / or transmit information. The one or more processors 114 of the system 110 may be implemented using any means for processing, such as one or more processing units, one or more processing devices, processors, computers, or programmable hardware components operable with appropriately adapted software. In other words, the described functions of the one or more processors 114 may also be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may include general-purpose processors, digital signal processors (DSPs), microcontrollers, etc. The one or more storage devices 116 of the system 110 may include at least one element of the following group: a computer-readable storage medium, such as a magnetic or optical storage medium, e.g., a hard disk drive, flash memory, floppy disk, 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] More detailed content and aspects of the system and ophthalmic microscope system are set forth in relation to the proposed concept or one or more examples described above or below (e.g., Figures 2-6). The system and / or ophthalmic microscope system 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.

[0058] 2 shows a flow chart of one example of a corresponding (computer-implemented) method for an ophthalmic microscope system. The method includes obtaining 210 imaging sensor data from an optical imaging sensor of a microscope of the ophthalmic microscope system. The method includes determining 220 information related to an anatomical feature of the eye indicated in the imaging sensor data. The method includes controlling 230 an illumination system of the ophthalmic microscope system to adjust characteristics of a light beam used for red reflex illumination of the eye based on the information related to the anatomical feature.

[0059] For example, the method may be performed by the system 110 and / or the ophthalmic microscope system 100 introduced in connection with Figures 1a to 1e. Features introduced in connection with the system 110 and / or the ophthalmic microscope system 100 may likewise be introduced in the corresponding method of Figure 2.

[0060] More detailed content and aspects of the method are referred to in relation to the proposed concept or one or more examples described above or below (e.g., Figures 1a-1e, 3-6). The method may comprise 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] 3 shows a block diagram of an example of components of the proposed ophthalmic microscope system. In this example, the ophthalmic microscope system includes a video camera 310 (e.g., at least one optical imaging sensor 122; 124 of the microscope 120 shown in Figs. 1a-1e), a processor 320 coupled to the video camera 310, a controller 330 coupled to the processor 320 (the processor 320 and the controller 330 may be implemented by the system 110), and an actuator 340 coupled to the controller 330 (e.g., an adjustable iris, a display device or a motor used to move the iris, a light source).

[0062] One or more video cameras 310 of the microscope can capture video of the surgical field, and the video signals can then be sent to a processor 320. The processor 320 can analyze the microscope's video content to detect the presence of an eyeball and identify its location in the video frame. The diameter of the limbus or pupil (i.e., anatomical features) can be automatically measured, and a corresponding red reflex iris diameter can be selected. The processor can then instruct the controller 330 to move and resize the red reflex iris by controlling the actuator 340.

[0063] The iris may be an opto-mechanical device mounted on a platform that can be shifted in a plane perpendicular to the illumination light path, as shown in Figure 4. Alternatively, a pixelated electronic modulator such as a liquid crystal display, a micromirror array panel, or a self-emissive display panel (e.g., display device 136) may be used, as shown in Figure 5. In such cases, the illumination pattern may remain circular, but may not need to be circular.

[0064] While detecting and locating the limbus and / or pupil, the processor may also measure the brightness of the red reflex and instruct the red reflex light controller to modify the illumination intensity (below a pre-set safety threshold) that provides a clear visualization, particularly in digital video. For example, during hydrodissection, the light intensity may be increased to maintain the clarity of the red reflex.

[0065] 4 is a schematic diagram of one example of a mechanically adjustable iris that may be part of the illumination system 130. The mechanically adjustable iris includes an adjustable iris diaphragm, the actual iris 134, mounted on a platform 410 that is movable in the xy directions, and which includes an aperture 420 that is aligned with or accommodates the iris 134. Shafts 430; 435 can be used to move the platform 410 in the x and y directions.

[0066] 5 shows a schematic diagram of one example of an electronically adjustable iris that may be part of the illumination system 130. Light 510 from a light source is emitted towards a reflective display device 136, and a portion 530 of the light that strikes a portion 520 of the reflective display device 136 is reflected towards a projection lens 540.

[0067] More detailed content and aspects of the ophthalmic microscope system are referred to in connection with the proposed concept or one or more examples described above or below (e.g., Figures 1a-2, 6). The ophthalmic microscope system 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.

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

[0069] Although 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 as described in relation to one or more of the figures of Figures 1 to 5. Alternatively, the microscope may be part of a system as described in relation to one or more of the figures of Figures 1 to 5 or may be connected to a system as described in relation to one or more of the figures of Figures 1 to 5. Figure 6 shows a schematic diagram of a system 600 configured to perform the methods described herein. The system 600 includes a microscope 610 and a computer system 620. The microscope 610 is configured to capture images and is connected to the computer system 620. The computer system 620 is configured to perform at least some of the methods described herein. The computer system 620 may be configured to execute machine learning algorithms. The computer system 620 and the microscope 610 may be separate entities, but may be integrated in one common housing. The computer system 620 may be part of a central processing system of the microscope 610 and / or the computer system 620 may be part of a subordinate part of the microscope 610, such as a sensor, actor, camera or lighting unit of the microscope 610.

[0071] The computer system 620 may be a local computing device (e.g., a personal computer, laptop, tablet computer, or mobile phone) with one or more processors and one or more storage devices, or may be a distributed computing system (e.g., a cloud computing system with one or more processors and one or more storage devices distributed at various locations, such as local clients and / or one or more remote server farms and / or data centers). The computer system 620 may include any circuit or combination of circuits. In one embodiment, the computer system 620 may include one or more processors, which may be of any type. As used herein, a processor may contemplate any type of computing circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA), or any other type of processor or processing circuit, for example, of a microscope or a microscope component (e.g., a camera). Other types of circuits that may be included in computer system 620 may be custom circuits, application specific integrated circuits (ASICs), such as one or more circuits (such as communications circuits) used in wireless devices such as cell phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. Computer system 620 may also 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 handling removable media, such as compact discs (CDs), flash memory cards, digital video discs (DVDs), and the like.Computer system 620 may also include a display device, one or more speakers and a keyboard and / or controller which may include a mouse, a trackball, a touch screen, a voice recognition device, or any other device that enables a user of the system to input information to and receive information from computer system 620.

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

[0073] Depending on certain implementation requirements, the embodiments of the present invention can be implemented in hardware or software. The implementation can be performed by a non-transitory recording medium, such as a digital recording medium, for example a floppy disk, a DVD, a Blu-ray, a CD, a ROM, a PROM and EPROM, an EEPROM or a FLASH memory, on which electronically readable control signals are stored, which cooperate (or can cooperate) with a programmable computer system to implement the respective methods. Thus, the digital recording medium can be computer readable.

[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 which is operable to perform any of the methods when the computer program product is run on a computer, the program code may for example be stored on a machine readable carrier.

[0076] Another embodiment comprises 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 present invention is a recording medium (or data carrier or computer readable medium) containing a computer program stored thereon for performing 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-transitory. Another embodiment of the present invention is an apparatus as described herein, including a processor and a recording medium.

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

[0082] Another embodiment of 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 storage device, etc. The apparatus or system may, for example, include a file server to transfer 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 functionality 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. In general, the methods are advantageously performed by any hardware apparatus.

[0084] Some embodiments of the present disclosure are based on the use of machine learning models or algorithms.

[0085] The machine learning model may be trained using training input data. The above example uses a training method called "supervised learning". In supervised learning, the 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" during training which output value to provide based on input samples that are similar to the provided sample. Besides 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 supervised learning algorithms (e.g. classification algorithms, regression algorithms or similarity learning algorithms). Classification algorithms may be used 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. Regression algorithms may be used when the output may have any numerical value (within a range). 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.

[0086] 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 (only) provided, and unsupervised learning algorithms 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 multiple subsets (clusters), such that input values ​​in the same cluster are similar according to one or more (predefined) similarity criteria, but are not similar to input values ​​contained in another cluster.

[0087] 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 their surroundings. Based on the actions taken, rewards are calculated. Reinforcement learning is based on training one or more software agents to select actions that result in an increasing cumulative reward (as manifested by an increasing reward), resulting in the software agent becoming better at a given task.

[0088] Furthermore, some techniques may be applied to parts of the machine learning algorithm. For example, feature representation learning may be used. In other words, the machine learning model may be trained at least in part with feature representation learning, and / or the 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, may not only preserve information in its input, but may also transform the information to make it useful, often as a pre-processing step before performing classification or prediction. Feature representation learning may be based on, for example, principal component analysis or cluster analysis.

[0089] 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, a machine learning model may be trained at least in part with anomaly detection and / or a machine learning algorithm may include an anomaly detection component.

[0090] In some examples, the 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, an observation about an item (e.g., a set of input values) may be represented by a branch of the decision tree, and an output value corresponding to this item may be represented by a leaf 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.

[0091] Association rules are another technique that may be used in machine learning algorithms. In other words, a machine learning model may 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 may identify and / or utilize one or more association rules that represent knowledge derived from the data. These rules may be used, for example, to store, manipulate, or apply the knowledge.

[0092] 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 set of data structures and / or rules that represent learned knowledge (e.g., based on training performed by a machine learning algorithm). In embodiments, the use of machine learning algorithm may refer to the use of an underlying machine learning model (or underlying machine learning models). The use of 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.

[0093] For example, the machine learning model may be an artificial neural network (ANN). An ANN is a system influenced by biological neural networks, such as those found in the retina or the brain. An ANN contains a number of interconnected nodes and a number of junctions, so-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 convey information from one node to another. The output of a node may be defined as a (non-linear) function of its inputs (e.g. the sum of its inputs). The inputs of a node may be used in a function based on the "weights" of the edges or nodes that provide the inputs. 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 to obtain a desired output for a given input.

[0094] 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 may 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 represent a set of random variables and their conditional dependencies using a directed acyclic graph. Alternatively, the machine learning model may be based on a genetic algorithm, which is a heuristic method that mimics search algorithms and the process of natural selection. [Explanation of symbols]

[0095] 10 Eyeball 12 Pupil 14 Iris 16 Sclera 17 Cornea 18 limbus 100 Ophthalmic Microscope System 105 Stand 110 System 112 One or more interfaces 114 One or more processors 116 One or more storage devices 120 Microscope 122;124 Optical imaging sensor 130 Lighting System 132 Light source 134 Iris 136 Display Devices 140a Eyepiece Display 140b Auxiliary Display 150 Arm 210 Acquiring Imaging Sensor Data 220 Determining information about anatomical features 230 Lighting System Control 310 Video Camera 320 Processor 330 Controller 340 Actuator 410 Platform 420 Aperture 430;435 Shaft 510 Light from a source 520 Part of a reflective display device 530 Light reflected towards the projection lens 540 Projection Lens 600 System 610 Microscope 620 Computer Systems

Claims

1. A system (110; 620) for an ophthalmic microscope system (100; 600), said system comprising one or more processors (114) and one or more storage devices (116), said system comprising: acquiring imaging sensor data from an optical imaging sensor (122; 124) of a microscope (120; 610) of said ophthalmic microscope system; determining information regarding anatomical features of the eye represented in the imaging sensor data; and controlling an illumination system (130) of the ophthalmic microscope system to adjust characteristics of a light beam used for red reflex illumination of the eye based on the information regarding the anatomical features. System (110;620).

2. the anatomical feature is a pupil of the eye, and the system is configured to determine information about the pupil of the eye and to control the illumination system to adjust the characteristics of the light beam based on the information about the pupil of the eye. The system of claim 1 .

3. the anatomical feature is a limbus of the eye, and the system is configured to determine information about the limbus of the eye and to control the illumination system to adjust the characteristics of the light beam based on the information about the limbus of the eye. The system of claim 1 .

4. the system is configured to determine a position of the anatomical feature of the eye and to control the illumination system to adjust a path of the light beam based on the position of the anatomical feature of the eye. A system according to any one of claims 1 to 3.

5. the system is configured to determine a size of the anatomical feature of the eye and to control the illumination system to adjust a width of the light beam based on the size of the anatomical feature of the eye. A system according to any one of claims 1 to 4.

6. the system is configured to determine a brightness or contrast of the red reflex illumination of the eye and to control the illumination system to adjust an intensity of the light beam based on the brightness or contrast of the red reflex illumination. A system according to any one of claims 1 to 5.

7. the system is configured to update the information regarding the anatomical features over successive frames of the imaging sensor data and to control the illumination system to adjust the characteristics of the light beam upon detection of a change in the information regarding the anatomical features of the eye. A system according to any one of claims 1 to 6.

8. The system is configured to detect the anatomical feature in the imaging sensor data using a machine learning model trained to detect the anatomical feature in imaging sensor data, and to determine the information about the anatomical feature based on an output of the machine learning model. A system according to any one of claims 1 to 7.

9. the output of the machine learning model includes at least one of information regarding the location of the anatomical feature and information regarding the contour; The system of claim 8.

10. the system is configured to control the illumination system to temporarily increase a beam width of the light beam and to determine the information about the anatomical feature based on imaging sensor data generated based on the increased beam width. A system according to any one of claims 1 to 9.

11. the system is configured to control the illumination system to temporarily disable the light beam, compare imaging sensor data generated while the light beam is disabled with imaging sensor data generated while the light beam is enabled, and determine the information regarding the anatomical feature based on the comparison. A system according to any one of claims 1 to 10.

12. A system (110; 620) according to any one of claims 1 to 11, said microscope (120; 610) and said illumination system (130), Ophthalmic microscope system (100;600).

13. the lighting system comprises one or more motors for adjusting a position of at least one of a light source (132) and an iris (134) of the lighting system, the system being configured to control the one or more motors to adjust a position of at least one of a light source and an iris of the lighting system in order to adjust a path of a light beam emitted by the lighting system.

13. An ophthalmic microscope system according to claim 12.

14. the illumination system comprises a display device (136) for modulating a light beam emitted by the illumination system, the system being configured to control the display device to adjust at least one of a path and a width of the light beam.

13. An ophthalmic microscope system according to claim 12.

15. the illumination system comprises a light source (132) configured to emit the light beam and the display device (136), the display device (136) configured to selectively reflect the light beam towards the eye, thereby modulating the light beam; 15. An ophthalmic microscope system according to claim 14.

16. the illumination system comprises a light source (132) configured to emit the light beam and the display device (136), the display device (136) configured to modulate the light beam as it passes through the display device and toward the eye; 15. An ophthalmic microscope system according to claim 14.

17. 1. A method for an ophthalmic microscope system, the method comprising: acquiring (210) imaging sensor data from an optical imaging sensor of a microscope of the ophthalmic microscope system; determining (220) information relating to anatomical features of the eye represented in the imaging sensor data; controlling (230) an illumination system of the ophthalmic microscope system to adjust characteristics of a light beam used for red reflex illumination of the eye based on the information regarding the anatomical features; The method includes:

18. 18. A computer program having a program code for performing the method according to claim 17, when the computer program is executed on a processor.

Images