Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

An image processing apparatus according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain volume data of a subject. The processing circuitry is configured to obtain base tubular object data by segmenting the volume data. The processing circuitry is configured to obtain small tubular object data from the volume data. The processing circuitry is configured to generate updated base tubular object data, on the basis of the small tubular object data and the base tubular object data. The processing circuitry is configured to output the updated base tubular object data.

An image processing apparatus according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain volume data of a subject. The processing circuitry is configured to obtain base tubular object data by segmenting the volume data. The processing circuitry is configured to obtain small tubular object data from the volume data. The processing circuitry is configured to generate updated base tubular object data, on the basis of the small tubular object data and the base tubular object data. The processing circuitry is configured to output the updated base tubular object data.

This invention provides a system and method for finding line features in an image that allows multiple lines to be efficiently and accurately identified and characterized. When lines are identified, the user can train the system to associate predetermined (e.g. text) labels with respect to such lines. These labels can be used to define neural net classifiers. The neural net operates at runtime to identify and score lines in a runtime image that are found using a line-finding process. The found lines can be displayed to the user with labels and an associated probability score map based upon the neural net results. Lines that are not labeled are generally deemed to have a low score, and are either not flagged by the interface, or identified as not relevant.

This invention provides a system and method for finding line features in an image that allows multiple lines to be efficiently and accurately identified and characterized. When lines are identified, the user can train the system to associate predetermined (e.g. text) labels with respect to such lines. These labels can be used to define neural net classifiers. The neural net operates at runtime to identify and score lines in a runtime image that are found using a line-finding process. The found lines can be displayed to the user with labels and an associated probability score map based upon the neural net results. Lines that are not labeled are generally deemed to have a low score, and are either not flagged by the interface, or identified as not relevant.