Abstract: One or more example embodiments provides a system and a method for differentiating a tissue of interest from another part of a medical scanner image, in particular pectoral muscle tissue from breast tissue in an X-ray mammography image. The method comprises providing a medical scanner image; inputting input data into a trained artificial neural network, the input data being based on the provided medical scanner image; generating, by the trained artificial neural network, output data based on the input data, the output data indicating a one-dimensional borderline between at least a part of the tissue of interest and the at least one other part of the medical scanner image; and outputting an output signal comprising or based on the generated output data.

Abstract: One or more example embodiments relates to a method for estimating a population uncertainty distribution of training data, comprising providing an artificial intelligence as an ensemble learner model comprising at least two base learners by an electronic computing device; providing the training data for training the ensemble learner model by the electronic computing device; separating the training data into a first set of training data for training one of the at least two base learners and into a second set of training data for validating another one of the at least two base learners by the electronic computing device; training the one of the at least one base learner with the first set of training data by the electronic computing device; and estimating the population uncertainty distribution by validating the another at least one base learner with the second set of training data by the electronic computing device.

Abstract: A computer-implemented method for detecting an out-of-distribution case, comprises: receiving medical imaging data including voxels and representing an anatomical region including an object set, the object set including at least one anatomical object; applying an epistemic Bayesian uncertainty model to the medical imaging data to determine an epistemic uncertainty information describing an epistemic uncertainty of assignment information describing an assignment of a respective voxel to a respective anatomical object; applying a scoring procedure to the epistemic uncertainty information to determine scoring information; and providing a warning signal if the scoring information satisfies an out-of-distribution condition.

Abstract: A method for detection and characterization of lesions includes acquiring a plurality of phase images of a multi-phase imaging exam, extracting a local context for each phase image of the plurality of phase images, encoding the local contexts to create phase specific feature maps, combining the phase-specific feature maps to create unified feature maps, and at least one of characterizing or detecting a lesion based on the unified feature maps.

Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.

Abstract: Various examples of the disclosure pertain to the characterization of one or more metastases using a neural network, as well as an apparent diffusion coefficient, ADC, map as obtained from diffusion-weighted magnetic resonance imaging, DWI MRI. A convolutional neural network can be employed. Training processes and inference processes are disclosed.

Abstract: One or more example embodiments of the present invention relates to Computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprising receiving input data, the input data comprising an X-ray image including the examining region; applying a first trained function to the input data to detect at least one region of interest in the X-ray image and to generate a heatmap comprising the at least one region of interest; applying a second trained function to the input data and the heatmap to generate an individual score for each of the at least one region of interest and to generate a score-weighted heatmap based on the at least one region of interest and the individual scores; applying a third trained function to the input data and the score-weighted heatmap to generate a positioning score; and providing the positioning score.

Abstract: A method for detection and characterization of lesions includes acquiring a plurality of phase images of a multi-phase imaging exam, extracting a local context for each phase image of the plurality of phase images, encoding the local contexts to create phase specific feature maps, combining the phase-specific feature maps to create unified feature maps, and at least one of characterizing or detecting a lesion based on the unified feature maps

Abstract: In a computer-implemented method, a machine-learning model is pre-trained in an unsupervised manner to predict time-related information based on data obtained from a contrast-enhanced medical imaging measurement. This pre-trained machine-learning model is then used to build another machine-learning model to predict semantic context information for images determined from the contrast-enhanced medical imaging measurement.

Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.

Abstract: One or more example embodiments provides a system and a method for differentiating a tissue of interest from another part of a medical scanner image, in particular pectoral muscle tissue from breast tissue in an X-ray mammography image. The method comprises providing a medical scanner image; inputting input data into a trained artificial neural network, the input data being based on the provided medical scanner image; generating, by the trained artificial neural network, output data based on the input data, the output data indicating a one-dimensional borderline between at least a part of the tissue of interest and the at least one other part of the medical scanner image; and outputting an output signal comprising or based on the generated output data.

Abstract: The present techniques provide for the processing of color tissue images based on image segmentation. In an exemplary embodiment, the color and texture features of pixels in a tissue image are used to generate a matrix of feature vectors. A subset of feature vectors is selected from the matrix of feature vectors and a set of colors and textures are derived using the tissue image and the subset of feature vectors. An initial segmented tissue image is then generated from this set of colors and textures.

Abstract: The present techniques provide for the processing of color tissue images based on image segmentation. In an exemplary embodiment, the color and texture features of pixels in a tissue image are used to generate a matrix of feature vectors. A subset of feature vectors is selected from the matrix of feature vectors and a set of colors and textures are derived using the tissue image and the subset of feature vectors. An initial segmented tissue image is then generated from this set of colors and textures.

Abstract: A method for imaging is presented. The method includes receiving a first image data set and at least one other image data set. Further the method includes adaptively selecting corresponding regions of interest in each of the first image data set and the at least one other image data set based upon apriori information associated with each of the first image data set and the at least one other image data set. Additionally, the method includes selecting a customized registration method based upon the selected regions of interest and the apriori information corresponding to the selected regions of interest. The method also includes registering each of the corresponding selected regions of interest from the first image data set and the at least one other image data set employing the selected registration method.