Techniques for facilitating testing analytics of imaging systems and methods using molds are provided. In one example, a system includes a mold temperature controller configured to apply a thermal signature to a mold of a target. The system further includes a focal plane array configured to capture an infrared image of the mold. The system further includes an image analytics device configured to determine thermal analytics associated with the mold based on the infrared image. Related devices and methods are also provided.

A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

An apparatus (10) for manually auditing a set (30) of images having quality ratings (38) for an image quality metric assigned to the respective images of the set of images by an automatic quality assessment process (40) includes at least one electronic processor (20) programmed to: generate quality rating confidence values (42) indicative of confidence of the quality ratings for the respective images; select a subset (32) of the set of images for manual review based at least on the quality rating confidence values; and provide a user interface (UI) (27) via which only the subset of the set of images is presented and via which manual quality ratings (46) for the image quality metric are received for only the subset of the set of images.

Systems and methods for generating a segmentation mask of an anatomical structure, along with a measure of uncertainty of the segmentation mask, are provided. In accordance with one or more embodiments, a plurality of candidate segmentation masks of an anatomical structure is generated from an input medical image using one or more trained machine learning networks. A final segmentation mask of the anatomical structure is determined based on the plurality of candidate segmentation masks. A measure of uncertainty associated with the final segmentation mask is determined based on the plurality of candidate segmentation masks. The final segmentation mask and/or the measure of uncertainty are output.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method for automatically classifying emission tomographic images includes receiving original images and a plurality of class labels designating each original image as belonging to one of a plurality of possible classifications and utilizing a data generator to create generated images based on the original images. The data generator shuffles the original images. The number of generated images is greater than the number of original images. One or more geometric transformations are performed on the generated images. A binomial sub-sampling operation is applied to the transformed images to yield a plurality of sub-sampled images for each original image. A multi-layer convolutional neural network (CNN) is trained using the sub-sampled images and the class labels to classify input images as corresponding to one of the possible classifications. A plurality of weights corresponding to the trained CNN are identified and those weights are used to create a deployable version of the CNN.

Augmenting real-time views of a patient with three-dimensional (3D) data. In one embodiment, a method may include identifying 3D data for a patient with the 3D data including an outer layer and multiple inner layers, determining virtual morphometric measurements of the outer layer from the 3D data, registering a real-time position of the outer layer of the patient in a 3D space, determining real-time morphometric measurements of the outer layer of the patient, automatically registering the position of the outer layer from the 3D data to align with the registered real-time position of the outer layer of the patient in the 3D space using the virtual morphometric measurements and using the real-time morphometric measurements, and displaying, in an augmented reality (AR) headset, one of the inner layers from the 3D data projected onto real-time views of the outer layer of the patient.

An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

An image processing apparatus selects one or a plurality of examinations to which a medical image belongs, determines image processing candidate examinations based on the selected one or plurality of examinations, displays medical images belonging to the determined image processing candidate examinations on a display unit, and executes image processing using, of the displayed medical images, a plurality of medical images selected by a user, wherein, when the one examination is selected, the selected one examination and one or a plurality of examinations obtained by a search based on the selected one examination are determined as the image processing candidate examinations, and when the plurality of examinations are selected, in the determining, the selected plurality of examinations are determined as the image processing candidate examinations.

An image processing apparatus includes a feature extraction unit, a reconstruction unit, an evaluation unit, and a control unit. The feature extraction unit inputs an input image to a feature extraction NN, and outputs an intermediate image from the feature extraction NN. The reconstruction unit inputs the intermediate image to an m-th reconstruction NN, and outputs an m-th output image from the m-th reconstruction NN. The evaluation unit obtains an evaluation value based on a sum of differences between the m-th tomographic image and the m-th output image. The control unit repeatedly performs processes of the feature extraction unit and the reconstruction unit, calculation of the evaluation value by the evaluation unit, and training of the feature extraction NN and the m-th reconstruction NN based on the evaluation value.