Abstract: A method and a device for training a machine learning model to construct a digital image depicting a sample are disclosed. The model is trained using a training set of images captured with a low numerical aperture (NA) objective. This set consists of multiple images of a sample, each taken under a different, structured illumination pattern. Concurrently, a ground truth image is captured. This is a single, high-resolution image of the same sample taken with a higher NA objective and conventional illumination. The machine learning model is then trained to use the series of low-resolution, uniquely illuminated images to construct a final image that matches the high-resolution ground truth. A microscope system that utilizes this trained model to produce high-resolution images surpasses the physical resolution limits of the objective lens used for capture.

Abstract: A method and a device are for training a machine learning model to construct a digital image depicting a focused sample. The method includes acquiring a training set of digital images of a training sample by: positioning the training sample, relative to a microscope objective, at a focus position outside a range of positions between a near limit and a far limit of a depth of field of the microscope objective; illuminating the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample; receiving a ground truth having a digital image depicting the training sample being in focus; and training the machine learning model to construct the digital image depicting a focused sample using the training set of digital images and the ground truth.

Abstract: The present inventive concept relates to a method and a device for training a machine learning model to construct a digital color image depicting a sample. The method comprising: acquiring a training set of digital images of a training sample by: illuminating, by a plurality of white light emitting diodes, the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample; receiving a ground truth comprising a high-resolution digital color image of the training sample, wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images; and training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth.

Abstract: The present disclosure relates to a method (40) and a device (10) for identifying a region of interest in a sample (192).

Abstract: Devices and methods for training a machine learning model to analyze a sample are provided. An example method comprises: receiving a ground truth comprising a classification of at least one portion of a sample; acquiring a training set of digital images of the sample by illuminating the sample from a plurality of directions and capturing a digital image for each of the plurality of directions; and training the machine learning model to analyze the sample using the training set of digital images and the received ground truth. Further, a microscope system and a method for analyzing a sample is provided.

Abstract: The present disclosure relates to methods and devices for training a machine learning model to construct a digital image depicting an artificially stained sample, the method comprising: receiving a training set of digital images of an unstained sample, wherein the training set of digital images is acquired by illuminating the unstained sample from a plurality of directions and capturing a digital image for each of the plurality of directions; receiving a ground truth comprising a digital image of a stained sample, wherein the stained sample is formed by applying a staining agent to the unstained sample; and training the machine learning model to construct a digital image depicting an artificially stained sample using the received training set of digital images of the unstained sample and the received ground truth. The present disclosure further relates to a microscope system and a method for constructing a digital image depicting an artificially stained sample.

Abstract: The disclosure relates to a method and corresponding system for digital microscopy imaging comprising: capturing with a camera, a plurality of images of a position in a biological sample, wherein each image is captured at a different focal distance, and determining in the camera, a focus value for each captured image. For each captured image, the focus value of the captured image is compared with at least one threshold focus value. Upon determining that the focus value of the captured image exceeds the at least one threshold focus value, the captured image is marked as an interesting image, and transmitted from the camera to a separate computing unit.

Abstract: In one embodiment of the present invention, a method is disclosed for determining a difference between a sample position and an in-focus position, as well as a vision inspection system. In a first step image data depicting a sample is captured. Next, a feature set is extracted from the image data. Thereafter, the feature set is classified into a position difference value, corresponding to the difference between the sample position and the in-focus position, by using a machine learning algorithm that is trained to associate image data features to a position difference value.

Abstract: The present invention relates to a method for changing the appearance of an original image comprising N>1 classes of image elements, the method comprising the step of: for each pixel and for at least one subset of the original image: calculating N probability values, each probability value defining a probability for the pixel of belonging to a corresponding one of the N classes of image elements, transforming a color of the pixel by using predetermined color transforms for each of the N classes of image elements and the N probability values for the pixel.

Abstract: The present invention relates to a method for changing the appearance of an original image comprising N>1 classes of image elements, the method comprising the step of: for each pixel and for at least one subset of the original image: calculating N probability values, each probability value defining a probability for the pixel of belonging to a corresponding one of the N classes of image elements, transforming a color of the pixel by using predetermined color transforms for each of the N classes of image elements and the N probability values for the pixel.

Abstract: The present invention relates to a method, an analyzer and a computer program product for focus prediction in a sample arranged on a measuring surface of an analyzer. The method comprises row wise scanning sample positions by means of an optical system of said analyzer, said sample positions being positions in a coordinate system for said measuring surface containing said sample, for the first sample position on each row, determining a focus and storing said focus, and for each subsequent sample position: if a stored difference exists, predicting a focus by adding said stored difference to said stored focus, or if no stored difference exists, determining a focus and storing said focus.

Abstract: The present invention relates to a method, an analyzer and a computer program product for focus prediction in a sample arranged on a measuring surface of an analyzer. The method comprises row wise scanning sample positions by means of an optical system of said analyzer, said sample positions being positions in a coordinate system for said measuring surface containing said sample, for the first sample position on each row, determining a focus and storing said focus, and for each subsequent sample position: if a stored difference exists, predicting a focus by adding said stored difference to said stored focus, or if no stored difference exists, determining a focus and storing said focus.

Abstract: In one embodiment of the present invention, a method is disclosed for determining a difference between a sample position and an in-focus position, as well as a vision inspection system. In a first step image data depicting a sample is captured. Next, a feature set is extracted from the image data. Thereafter, the feature set is classified into a position difference value, corresponding to the difference between the sample position and the in-focus position, by using a machine learning algorithm that is trained to associate image data features to a position difference value.