In high-resolution scanning microscopy, a sample is excited by illumination radiation to emit fluorescence radiation in such a way that the illumination radiation is focused at a point in or on the sample to form a diffraction-limited illumination spot. The point is imaged in a diffraction-limited manner into a diffraction image on a spatially resolving surface detector, wherein the surface detector has a spatial resolution that resolves a structure of the diffraction image. The sample is scanned by means of different scanning positions with an increment of less than half the diameter of the illumination spot. An image of the sample is generated from the data of the surface detector and from the scanning positions assigned to said data, said image having a resolution that is increased beyond a resolution limit for imaging. For the purposes of distinguishing between at least two predetermined wavelength regions in the fluorescence radiation from the sample, a corresponding number of diffraction structures are generated on the surface detector for the at least two predetermined wavelength ranges, said diffraction structures differing but having a common center of symmetry. The diffraction structures are evaluated when generating the image of the sample.

A method for training a mathematical model which describes a light propagation in a reflection microscopy includes radiating a light distribution I.sub.0 into an excitation path of a microscope, modulating the light distribution I.sub.0 to form a light distribution I.sub.A in the excitation path via an optical modulator, reflecting the light distribution I.sub.A at a location of a sample in a detection path of the microscope, modulating the light distribution I.sub.A to form a light distribution I.sub.D in the detection path via a further optical modulator, recording a reflected light distribution I.sub.D, repeating the above steps n-fold to generate an n-fold 3-tuple (M.sub.A, M.sub.D; I.sub.D), transferring the n-fold 3-tuple (M.sub.A, M.sub.D; I.sub.D) to a computer to implement a mathematical model F for a light propagation in reflection microscopy, and ascertaining the mathematical model F which describes the light propagation in reflection microscopy based on the n-fold 3-tuple (M.sub.A, M.sub.D; I.sub.D).

An optical device may include an optical fiber having a fixed end and a free end a first actuator positioned at a actuator position between the fixed end and the free end and configured to apply a first force on the actuator position of the optical fiber such that a movement of the free end of the optical fiber in a first direction is caused, wherein the first direction is orthogonal to a longitudinal axis of the optical fiber; and a deformable rod disposed adjacent to the optical fiber, and having a first end and a second end, wherein the first end is connected to a first rod position of the optical fiber and the second end is connected to a second rod position of the optical fiber.

This disclosure is directed to a system and method for changing an angle of incidence of a filter or filter wheel.

An oblique plane microscope includes an optical imaging system configured to form an image of an object. The optical imaging system includes a telescope system with an optical zoom system, which is adjustable for adapting a magnification of the telescope system to a ratio between two refractive indices, one of which being associated with an object side of the telescope system and the other being associated with an image side of the telescope system. The oblique plane microscope further includes a control unit. The control unit is configured to evaluate an image quality of the image formed by the optical imaging system and to adjust the optical zoom system based on the evaluation.

An image-guided tool and method for ophthalmic surgical procedures is disclosed comprising a processor, a display, an imaging system, and a memory communicatively coupled to the processor. The memory stores instructions executable by the processor and includes an artificial intelligence (AI) model. The processor is arranged to receive from the imaging system visual images in real-time of a surgical field during the ophthalmic surgical procedure and using the AI model to extract regions of interest in the surgical field. Upon selection of a region of interest by the AI model, the AI model develops operating image features based on the surgical instruments used in the region of interest and the phase of the surgical procedure being performed. Augmented visual images are then constructed that include the real-time visual image and the image features and surgical phase information. The augmented image is displayed on the display.

Disclosed herein are light sheet imaging systems for imaging fluorescent samples. Also disclosed herein are sample holder systems for high throughput light sheet imaging of multiple three-dimensional samples without user intervention. Further disclosed herein are automated image processing methods to identify and quantify fluorescent particles within three-dimensional image sets without user intervention or user bias.

An inverted microscope apparatus includes an immersion objective, an electric stage that moves at least in a direction orthogonal to an optical axis of the immersion objective, and a removal mechanism that removes an immersion liquid adhering to a bottom surface of a container placed on the electric stage. The removal mechanism is configured to scan the bottom surface using movement of the electric stage.

Systems and methods for Broad Line Fundus Imaging (BLFI), an imaging approach that is a hybrid between confocal and widefield imaging systems, are presented. These systems and methods are focused on improving the quality and signal of broad line fundus images or imaging methods to create high contrast and high resolution fundus images. Embodiments related to improved pupil splitting, artifact removal, reflex minimization, adaptable field of view, instrument alignment and illumination details are considered.

A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.