A solution is proposed for assisting a medical procedure. A corresponding method comprises acquiring a luminescence image (205F), based on a luminescence light, and an auxiliary image (205R), based on an auxiliary light different from this luminescence light, of a field of view (103); the field of view (103) contains a region of interest comprising a target body of the medical procedure (containing a luminescence substance) and one or more foreign objects. An auxiliary informative region (210Ri) representative of the region of interest without the foreign objects is identified in the auxiliary image (205R) according to its content, and a luminescence informative region (210Fi) is identified in the luminescence image (205F) according to the auxiliary informative region (210Ri). The luminescence image (205F) is processed limited to the luminescence informative region (210Fi) for facilitating an identification of a representation of the target body therein. A computer program and a corresponding computer program product for implementing the method are also proposed. Moreover, a computing device for performing the method and an imaging system comprising it are proposed. A medical procedure based on the same solution is further proposed.

An imaging system may include: a first light source configured to emit a first source spectrum of collimated light; a second light source configured to emit a second source spectrum of light; a probe head configured to direct the first source spectrum and the second source spectrum toward tissue in an oral cavity and to collect a first feedback spectrum of light and a second feedback spectrum of light; an interferometry sub-system to generate an optical feedback signal using the first source spectrum; at least one optical sensor array for receiving the optical feedback signal and the second feedback spectrum; and at least one programmable processor to generate: a first diagnostic image of the tissue using the optical feedback signal; a second diagnostic image of the tissue using the second feedback spectrum; and a third diagnostic image from a combination of the first diagnostic image and the second diagnostic image.

Various techniques are provided for performing automated full-cell segmentation and labeling in immunofluorescent microscopy. These techniques perform membrane segmentation and nuclear seed detection separate and independently from each other, then combine their results to identify cell boundaries. Some embodiments use texture- and kernel-based image processing to perform the method. In some embodiments, the method for obtaining membrane features disclosed herein can be used in conjunction with or separate from the nuclear features. The results can be used for a variety of purposes, including whole-area cell segmentation in fluorescence-based tissue imaging.

Flow through tubular organs (e.g., the esophagus) is analyzed based on fluid mechanics analysis of medical images. Using computational fluid dynamics, a reduced-order model is constructed and implemented to predict flow rate and fluid pressure developed inside flexible tubular organs inside the body. As one non-limiting example, the constructed model can be applied to analyze esophageal transport using fluoroscopy image sequences to predict flow rate, pressure, esophagus wall stiffness, and active relaxation.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

A computer-implemented method for predicting digital images in the form of a digital fluorescence representation together with a further derived representation by means of a combined machine learning system is described. The method comprises providing a first digital image of a tissue sample that was recorded under white light by means of a microsurgical optical system with a digital image recording unit, and predicting a second digital image of the tissue sample in a fluorescence representation and a further representation, which has optical indications about diseased tissue elements. This is done by means of a previously trained combined machine learning system comprising a trained combined machine learning model for predicting the second digital image of the tissue sample in the fluorescence representation and the further representation.

Offset illumination using multiple emitters in a fluorescence imaging system is described. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation. The emitter comprises a first emitter and a second emitter for emitting different wavelengths of electromagnetic radiation. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises one or more of a hyperspectral emission, a fluorescence emission, and/or a laser mapping pattern.

A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.

Systems, apparatuses, and methods are described herein for disease detection using an analyte-agnostic approach. Such systems, apparatuses, and methods can include using an array with hydrogels disposed on a substrate, where the hydrogels include one or more polymerized monomers and one or more photoinitiators or photocleavage products thereof. One or more samples including one or more unlabeled analytes can be contacted with an array of polymers. The samples disposed on the array can be incubated for a first predetermined period of time, and heated at a predetermined temperature for a second predetermined period of time. An imaging device (e.g., flatbed scanner) can be used to measure an amount of one or more colorimetric or luminescence signals produced by the array after the incubating and heating. A neural network trained using the samples can then be used to predict a diagnostic or disease class for the sample.

Examples relate to a system, a method and a computer program for training a machine-learning model, to a machine-learning model, a method and computer program for detecting at least one property of a sample of organic tissue, and to a microscope system. The system comprises one or more storage modules and one or more processors. The system is configured to obtain a plurality of images of a sample of organic tissue. The plurality of images are taken using a plurality of different imaging characteristics. The system is configured to train a machine-learning model using the plurality of images. The plurality of images are used as training samples and information on at least one property of the sample of organic tissue is used as a desired output of the machine-learning model. The machine-learning model is trained such that the machine-learning model is suitable for detecting the at least one property of the sample of organic tissue in image input data reproducing (only) a proper subset of the plurality of different imaging characteristics. The system is configured to provide the machine-learning model.