Systems and methods relate to processing optical tomography coherence (OCT) images to predict characteristics of a treatment to be administered to effectively treat age-related macular degeneration. The processing can include pre-processing the image by flattening and/or cropping the image and processing the pre-processed image using a neural network. The neural network can include a deep convolutional neural network. An output of the neural network can indicate a predicted frequency and/or interval at which a treatment (e.g., anti-vascular endothelial growth factor therapy) is to be administered so as to prevent leakage of vasculature in the eye.

In one example, a method of generating and delivering environmental data includes receiving a request for requested data corresponding to a first environmental characteristic and a second environmental characteristic. The method includes retrieving a first set of data corresponding to the first environmental characteristic. The method includes retrieving a second set of data corresponding to the second environmental characteristic. The method includes generating an image having a first channel and a second channel. The values of the first channel correspond to the first set of data. The values of the second channel correspond to the second set of data.

In one example, a method of generating and delivering environmental data includes receiving a request for requested data corresponding to a first environmental characteristic and a second environmental characteristic. The method includes retrieving a first set of data corresponding to the first environmental characteristic. The method includes retrieving a second set of data corresponding to the second environmental characteristic. The method includes generating an image having a first channel and a second channel. The values of the first channel correspond to the first set of data. The values of the second channel correspond to the second set of data.

To estimate the top surface of a measurement target on the basis of a data point group that corresponds to the top surface of a measurement target and is obtained using a laser scanner. This top surface estimation method for hoisting loads and by acquiring, using the laser scanner, data point groups in a hoisting load region which includes a hoisting load and an object from above the hoisting load and the object, dividing the hoisting load region into layers which constitute a plurality of groups which have a prescribed thickness in the vertical direction, and allocating the acquired data point groups to the plurality of layer groups, and estimating the top surfaces of the hoisting load and the object in each layer group on the basis of the data point groups allocated to the plurality of layer groups.

To estimate the top surface of a measurement target on the basis of a data point group that corresponds to the top surface of a measurement target and is obtained using a laser scanner. This top surface estimation method for hoisting loads and by acquiring, using the laser scanner, data point groups in a hoisting load region which includes a hoisting load and an object from above the hoisting load and the object, dividing the hoisting load region into layers which constitute a plurality of groups which have a prescribed thickness in the vertical direction, and allocating the acquired data point groups to the plurality of layer groups, and estimating the top surfaces of the hoisting load and the object in each layer group on the basis of the data point groups allocated to the plurality of layer groups.

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

The present disclosure discloses a three-dimensional data alignment apparatus, a three-dimensional data alignment method, and a recording medium, which may align a location between volumetric data and surface data even without a segmentation process of extracting a surface from the volumetric data. A three-dimensional data alignment apparatus according to an exemplary embodiment of the present disclosure includes a three-dimensional data alignment unit for aligning a location between first three-dimensional data and second three-dimensional data expressed in different data forms with regard to a target to be measured. The first three-dimensional data are three-dimensional data acquired in a voxel form with regard to the target to be measured, and the second three-dimensional data are three-dimensional data acquired in a surface form with regard to the target to be measured. The three-dimensional data alignment unit is configured to extract one or more vertices from the second three-dimensional data; extract the first voxel values of first voxels located around each vertex from the first three-dimensional data, based on a location of each vertex extracted from the second three-dimensional data; determine corresponding points between the first three-dimensional data and the second three-dimensional data based on the first voxel values extracted from the first three-dimensional data; and calculate location conversion information minimizing a location error between the first three-dimensional data and the second three-dimensional data based on the corresponding points.

Described herein is a method and system for automated, context sensitive and non-intrusive insertion of consumer-adaptive content in video. It assesses ‘context’ in the video that a consumer is viewing through multiple modalities and metadata about the video. The method and system described herein analyzes relevance for a consumer based on multiple factors such as the profile information of the end-user, history of the content, social media and consumer interests and professional or educational background, through patterns from multiple sources. The system also implements local-context through search techniques for localizing sufficiently large, homogenous regions in the image that do not obfuscate protagonists or objects in focus but are viable candidate regions for insertion for the intended content. This makes relevant, curated content available to a user in the most effortless manner without hampering the viewing experience of the main video.

Systems and methods for articular cartilage thickness mapping and lesion quantification operate on 3D medical image data to reconstruct cartilage surfaces, estimate surface normals, determine cartilage thickness, and identify regions of full-thickness cartilage loss (FCL). Reconstructed cartilage surfaces can be parcellated into subregions using a rule-based approach.