A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

A cross reality system enables any of multiple devices to efficiently and accurately access previously stored maps and render virtual content specified in relation to those maps. The cross reality system may include a cloud-based localization service that responds to requests from devices to localize with respect to a stored map. The request may include one or more sets of feature descriptors extracted from an image of the physical world around the device. Those features may be posed relative to a coordinate frame used by the local device. The localization service may identify one or more stored maps with a matching set of features. Based on a transformation required to align the features from the device with the matching set of features, the localization service may compute and return to the device a transformation to relate its local coordinate frame to a coordinate frame of the stored map.

The present disclosure relates to a device for contactless optical imaging of features of a hand, wherein the device comprises an illumination arrangement for illuminating a measuring site with light of substantially a first wavelength and with light of at least substantially a second wavelength. The device further comprising a camera comprising a detector and objective configured for imaging the measuring site on the detector. Within the measuring site a region of depth of field of the objective with respect to the first wavelength overlaps with a region of depth of field of the objective with respect to the second wavelength.

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.

A peak label object detection system (PLODS) includes an object size database configured to store information related to object size for a plurality of objects. The PLODS further includes a three-dimensional (3D) sensor database configured to store information related to parameters of a 3D sensor. The PLODS further includes an annotation database configured to store ground truth annotation information for images. The PLODS further includes a peak shape parameter calculator configured to determine a peak label size based on object size from the object size database and the parameters of the 3D sensor. The PLODS further includes a label generator configured to generate a peak labels map based on label size and the ground truth annotation information.

A system for determining an optimal hardness of a patient interface device includes a fit score determination unit structured to receive a 3-D model of the patient interface device and a 3-D model of a patient's face and to determine a fit score between the patient interface device and the patient's face based on the 3-D model of the patient interface device and the 3-D model of the patient's face, and a hardness determination unit structured to determine a hardness value of the patient interface device based on the determined fit score.

An image alignment apparatus includes at least one processor, and the processor derives, for each of first and second three-dimensional images each including a plurality of tomographic images and a common structure, first and second three-dimensional coordinate information that define an end part of the structure in a direction intersecting the tomographic image. The processor aligns the first three-dimensional image and the second three-dimensional image by using the first and second three-dimensional coordinate information to align the common structure included in each of the first three-dimensional image and the second three-dimensional image at least in the direction intersecting the tomographic image.

A data processing method that is suitable for obtaining quantitative information from data obtained by OCT imaging. The image processing method includes acquiring original image data corresponding to a three-dimensional image of a cultured embryo obtained by optical coherence tomography imaging of the embryo and executing a region segmentation the three-dimensional image into a plurality of regions on the basis of the original image data. In the region segmentation, a local thickness calculation is performed on the three-dimensional image to determine an index value indicating a size of an object included in the three-dimensional image, the three-dimensional image is segmented into a region indicated by the index value greater than a predetermined first threshold and a region indicated by the index value less than the first threshold, and each of the regions resulting from the segmentation is further segmented by the watershed algorithm.

In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.