A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

A method for fracture dynamic hydraulic properties estimation and reservoir simulation may include obtaining a first set of images of a first fracture. The method may include obtaining a first set of fracture detections from the first set of images, generating a plurality of numerical calculations based on the first set of fracture detections, and generating a second model based on the plurality of numerical calculations and the first set of fracture detections. The method may further include obtaining a second set of images of a second fracture of a new reservoir, generating a second set of fracture detections of the second fracture, and generating dynamic hydraulic estimations of the second fracture. The method may also include generating a three-dimensional reservoir simulation and determining a plurality of recovery schemes for the new reservoir.

The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.

Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.

One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein. Examples of AI applications discussed herein, include, but are not limited to, using one or more of: AI coregistration, AI marker detection, deep or machine learning, computer vision or image recognition task(s), keypoint detection, feature extraction, model training, input data preparation techniques, input mapping to the model, post-processing, and/or interpretation of output data, one or more types of machine learning models (including, but not limited to, segmentation, regression, combining or repeating regression and/or segmentation), marker detection success rates, and/or coregistration success rates to improve or optimize marker detection and/or coregistration.

A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.