Abstract: Presently disclosed are systems and methods for extrapolating a predictive two-dimensional (2D) segmentation to indicate a predicted region of interest (ROI) in a 2D medical image frame rendered from a set of three-dimensional (3D) medical scan data, wherein the predictive segmentation is based on a user-defined 2D segmentation that annotates the ROI in another image frame rendered from the 3D medical scan data. Present embodiments can utilize a user-defined or user-modified 2D segmentation of a frame to extrapolate predicted 2D segmentations for all of the other frames within a set of 3D medical scan data. This enables present embodiments to effectively annotate the ROI in a series of 2D medical image frames with minimal input from a clinician, reducing the amount of time the clinician spends analyzing the 3D medical scan data.

Abstract: Systems/techniques that facilitate dynamic user-interface comparison between machine learning output and training data are provided. In various embodiments, a system can access a data candidate on which a machine learning classifier is to be executed. In various aspects, the system can generate, via execution of the machine learning classifier, a predicted classification based on the data candidate. In various instances, the system can render, on an electronic display, a confidence graph that visually depicts how well or how poorly the data candidate and the predicted classification fit into an annotated training dataset on which the machine learning classifier has been trained.

Abstract: Techniques are described that facilitate interactive segmentation of medical images with low latency in a cloud-based medical imaging application. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component that receives a compressed representation of a three-dimensional (3D) medical image generated from the 3D medical image by an encoder network of a deep neural network model comprising an encoder network and a decoder network, an interaction component that receives user input relative to a displayed portion of the 3D medical image indicating an object included in the displayed portion for segmentation, and a segmentation component that generates a segmentation mask for the object based on the user input using the compressed representation and the decoder network.

Abstract: Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

Abstract: A 3D segmentation editing system accurately updates the segmentations of non-edited images of a 3D scan to reflect segmentation edits applied to other images of the scan using localized interpolation. In one or more embodiments, rather than replacing the entireties of the initial segmentations of non-edited images with newly generated, globally interpolated segmentations, the segmentation editing system applies a distance-based criterion to the interpolation of segmentation edits, such that only portions of the segmentations of the non-edited images that correspond to areas that were manually annotated in the edited images will be modified by the interpolation process, and the initial segmentations will be maintained outside of those edited areas. In this way, the system merges the interpolated segmentation with the initial segmentation for each non-edited image in a manner that mitigates unreliable modifications to the initial segmentations in areas far from the edited areas.

Abstract: Apparatus, systems, and methods to generate an edge aware brush for navigation and segmentation of images via a user interface are disclosed. An example processor is to at least: construct a brush for segmentation of image data; provide an interactive representation of the brush with respect to the image data via a user interface, the interactive representation to be displayed and made available for interaction in each of a plurality of viewports provided for display of views of the image data in the user interface; enable update of the viewports based on manipulation of the representation; facilitate display of a preview of a segmentation of the image data corresponding to a location of the representation; and, when the segmentation is confirmed, facilitate generation of an output based on the segmentation.

Abstract: Apparatus, systems, and methods to generate an edge aware brush for navigation and segmentation of images via a user interface are disclosed. An example processor is to at least: construct a brush for segmentation of image data; provide an interactive representation of the brush with respect to the image data via a user interface, the interactive representation to be displayed and made available for interaction in each of a plurality of viewports provided for display of views of the image data in the user interface; enable update of the viewports based on manipulation of the representation; facilitate display of a preview of a segmentation of the image data corresponding to a location of the representation; and, when the segmentation is confirmed, facilitate generation of an output based on the segmentation.

Abstract: Apparatus, systems, and methods to generate an edge aware brush for navigation and segmentation of images via a user interface are disclosed. An example processor is to at least: construct a brush for segmentation of image data; provide an interactive representation of the brush with respect to the image data via a user interface, the interactive representation to be displayed and made available for interaction in each of a plurality of viewports provided for display of views of the image data in the user interface; enable update of the viewports based on manipulation of the representation; facilitate display of a preview of a segmentation of the image data corresponding to a location of the representation; and, when the segmentation is confirmed, facilitate generation of an output based on the segmentation.

Abstract: Apparatus, systems, and methods to generate an edge aware brush for navigation and segmentation of images via a user interface are disclosed. An example processor is to at least: construct a brush for segmentation of image data; provide an interactive representation of the brush with respect to the image data via a user interface, the interactive representation to be displayed and made available for interaction in each of a plurality of viewports provided for display of views of the image data in the user interface; enable update of the viewports based on manipulation of the representation; facilitate display of a preview of a segmentation of the image data corresponding to a location of the representation; and, when the segmentation is confirmed, facilitate generation of an output based on the segmentation.

Abstract: The method of estimating a quality index of a gray-scale encoded three-dimensional image of a composite material part includes an obtaining step for obtaining a histogram from the image, the histogram representing for each class of a plurality of classes each including at least one gray-scale level, the number of voxels of the image having a gray-scale level belonging to the class; an extraction step for extracting a predetermined number of Gaussian functions present in the histogram; and an estimation step for estimating at least one quality index of the image from parameters characterizing the Gaussian functions extracted from the histogram.

Abstract: A method of characterizing a part made of woven composite material, includes calculating autocorrelation values of a three-dimensional image in an observation window of the volume of the part for a plurality of spatial vectors used as the autocorrelation interval, and then detecting at least one local extremum in the autocorrelation value on a main orientation of the weaving in order to determine a mean in the observation window for the distance between neighboring parallel yarns.

Abstract: Method and system for non-intrusive measurement of volume density of a specific phase in a part, comprising: processor producing a volume image of the part, the image being formed by a three-dimensional grid of voxels, the values of which indicate the disposition of the specific phase in the part, processor associating a binary coefficient with each voxel of the volume image, thus constructing an initial three-dimensional matrix representation of binary coefficients representing a presence or absence of the specific phase in zones of the part corresponding to the voxels, processor convoluting the initial matrix representation with a convolution matrix kernel corresponding to a predetermined reference volume, the convolution performed by effecting a composition of three (successive) monodimensional convolutions in three independent directions, thus forming a resultant matrix representation, each resultant coefficient of which represents a volume ratio (the density) of the specific phase in the reference volume

Abstract: The method of estimating a quality index of a gray-scale encoded three-dimensional image of a composite material part includes an obtaining step for obtaining a histogram from the image, the histogram representing for each class of a plurality of classes each including at least one gray-scale level, the number of voxels of the image having a gray-scale level belonging to the class; an extraction step for extracting a predetermined number of Gaussian functions present in the histogram; and an estimation step for estimating at least one quality index of the image from parameters characterizing the Gaussian functions extracted from the histogram.

Abstract: Method and system for non-intrusive measurement of volume density of a specific phase in a part, comprising: processor producing a volume image of the part, the image being formed by a three-dimensional grid of voxels, the values of which indicate the disposition of the specific phase in the part, processor associating a binary coefficient with each voxel of the volume image, thus constructing an initial three-dimensional matrix representation of binary coefficients representing a presence or absence of the specific phase in zones of the part corresponding to the voxels, processor convoluting the initial matrix representation with a convolution matrix kernel corresponding to a predetermined reference volume, the convolution performed by effecting a composition of three (successive) monodimensional convolutions in three independent directions, thus forming a resultant matrix representation, each resultant coefficient of which represents a volume ratio (the density) of the specific phase in the reference volume

Abstract: A method of characterizing a part made of woven composite material, includes calculating autocorrelation values of a three-dimensional image in an observation window of the volume of the part for a plurality of spatial vectors used as the autocorrelation interval, and then detecting at least one local extremum in the autocorrelation value on a main orientation of the weaving in order to determine a mean in the observation window for the distance between neighboring parallel yarns.