Abstract: A method for generating an adjusted (or augmented) 3D representation of an object based on a reference 3D source representation compliant with a source imaging modality and a plurality of reference target images compliant with a target imaging modality and establishing meaningful anatomical correspondences between the 3D object representation and the object's 2D partial/sparse view, including: obtaining, from reference target images, corresponding source images compliant with the source imaging modality; obtaining a sparse 3D source representation whose 2D sections correspond to the obtained source images; determining, from the reference 3D source representation and the sparse 3D source representation, a deformation field to be applied to voxels of the reference 3D source representation to register 2D sections of the reference 3D source representation with corresponding 2D sections of the sparse 3D source representation; obtaining the object's adjusted 3D representation by applying the deformation field to th

Abstract: Disclosed are systems and methods for classifying brain lesions based on single point in time imaging, methods for training a machine learning model for classifying brain lesions, and a method of predicting formation of brain lesions based on single point in time imaging. A method of classifying brain lesions based on single point in time imaging can include; accessing patient image data from a single point in time; providing the patient image data as an input to a brain lesion classification model; generating a classification for each of one or more lesions identified in the patient image data; and providing the classification for each of the one or more lesions for display on one or more display devices; wherein the brain lesion classification model is trained using subject image data for a plurality of subjects, the subject image data being captured at two or more points in time.

Abstract: Images are synthesized from a source to a target nature through unsupervised machine learning (ML), based on an original training set of unaligned source and target images, by training a first ML architecture through an unsupervised first learning pipeline applied to the original set, to generate a first trained model and induced target images consisting in representations of original source images compliant with the target nature. A second ML architecture is trained through a supervised second learning pipeline applied to an induced training set of aligned image pairs, each including first and second items corresponding respectively to an original source image and the induced target image associated with the latter, to generate a second trained model enabling image syntheses from the source to the target nature. Also, applications to effective medical image translations.

Abstract: The invention concerns a method for deformable fusion of a source multi-dimensional image (s(x)) and a target multi-dimensional image (t(x)) of an object, each image being defined on a multi-dimensional domain by a plurality of image signal samples, each sample having an associate position in the multi-dimensional domain and an intensity value, the method comprising estimating a smooth deformation field (d(x)) that optimizes a similarity criterion between the source image and the target image using a Markov Random Field framework, in near real-time performance. The similarity criterion is computed on transform coefficients obtained by applying a sub-space hierarchical transform to the image samples of the target image and to image samples obtained from the source image, an optimal tradeoff between a smoothness condition and the similarity criterion being automatically determined.

Abstract: The invention concerns a method for deformable fusion of a source multi-dimensional image (s(x)) and a target multi-dimensional image (t(x)) of an object, each image being defined on a multi-dimensional domain by a plurality of image signal samples, each sample having an associate position in the multi-dimensional domain and an intensity value, the method comprising estimating a smooth deformation field (d(x)) that optimizes a similarity criterion between the source image and the target image using a Markov Random Field framework, in near real-time performance. The similarity criterion is computed on transform coefficients obtained by applying a sub-space hierarchical transform to the image samples of the target image and to image samples obtained from the source image, an optimal tradeoff between a smoothness condition and the similarity criterion being automatically determined.

Abstract: A method and device for elastic registration of a two-dimensional source digital image of an object of interest with a slice of a three dimensional target volume of the object of interest is provided which defines a Markov Random Field framework having at least one undirected pairwise graph superimposed a two-dimensional image domain having at least a set of regular vertices and at least a set of edges, and defines a grid of control points, each control point corresponding to a vertex of the set of regular vertices, and a neighborhood system of edges associated with vertices. The method also defines a set of multi-dimensional labels of a discrete space to apply a displacement to each control point, the control point displacement defining a transformation adapted to obtain a plane slice of the target volume and an in-plane deformation transformation of the source digital image.

Abstract: A message passing scheme for MAP inference on Markov Random Fields based on a message computation using an intermediate input vector I, an output message vector M, an auxiliary seed vector S, all of equal length N, and a pairwise function r=d(x,y), where r, x, y are real numbers, includes: for each element j of vector S, do S(j)=j consider an index distance ?=2^floor(log 2(N)); repeat while ?>0 for each index of vector I, namely i, do in parallel: consider the set of all indices within distance ? from a given i, augmented by i; for every k belonging to this set, calculate its distance from i using the function: d(i,k)+I(S(k)); find the minimum distance and call n the index corresponding to this minimum distance do S(i)=S(n) ?=floor (?/2) for each j of vector M, do M(j)=I(S(j))+d(j,S(j)).

Abstract: The invention concerns a method and device for elastic registration of a two-dimensional source digital image of an object of interest with a slice of a three dimensional target volume of the object of interest, comprising defining a Markov Random Field framework comprising at least one undirected pairwise graph superimposed on the two-dimensional image domain comprising at least a set of regular vertices and at least a set of edges, defining at least a grid of control points, each control point corresponding to a vertex of the set of vertices, and a neighborhood system of edges associated with vertices. The method of the invention comprises defining a set of multi-dimensional labels of a discrete space to apply a displacement to each control point, a control point displacement defining a transformation adapted to obtain a plane slice of the target volume and an in-plane deformation transformation of the source digital image.

Abstract: A message passing scheme for MAP inference on Markov Random Fields based on a message computation using an intermediate input vector I, an output message vector M, an auxiliary seed vector S, all of equal length N, and a pairwise function r=d(x,y), where r,x,y are real numbers, includes: for each element j of vector S, do S(j)=j consider an index distance ?=2?floor(log2(N)); repeat while ?>0 for each index of vector I, namely i, do in parallel: consider the set of all indices within distance A from a given i, augmented by i; for every k belonging to this set, calculate its distance from i using the function: d(i,k)+I(S(k)); find the minimum distance and call n the index corresponding to this minimum distance do S(i)=S(n) ?=floor (?/2) for each j of vector M, do M(j)=I(S(j))+d(j,S(j)).

Abstract: A method for determining an optimal labeling of pixels in computer vision includes modeling an image by a graph having interior nodes and edges where each image point p is associated with a graph node, each pair of nearest neighbor points p, q is connected by a graph edge, each graph node p is associated with a singleton potential c(p), and each graph edge is associated with a pairwise potential function d(p,q). A label is randomly assigned to each point to initialize unary variables including an indicator function that indicates which label is assigned to which point and dual variables including height variables associated with each node p and label a, and balance variables associated with each edge (p,q) and label a. For each label, a new label c is selected, a capacitated graph is constructed and solved. The label selection divides the image into disjoint regions.

Abstract: A method for registering digitized images using Markov Random Fields (MRFs) includes providing a source image f and a target image g, defining a deformation grid of control points, defining a coordinate transformation as T ? ( x ) = x + ? p ? G ? ? ? ( ? x - p ? ) ? d u p , where x is a point on the source image, p is a position vector of control point p, dp is a displacement vector for each control point, up is a label for point p associated with displacement dp, and ?( ) is a weighting function for the displacement vector, defining an MRF energy functional to be minimized by T as E t = 1 ? G ? ? ? p ? G ? V p t ? ( u p ) + 1 ? E ? ? ? p , q ? E ? V pq ? ( u p , u q ) , wherein |G| is a number of control points, |E| is a number of pairs of neighboring control points on a neighborhood system, t is an iteration counter, and associating the MRF with a primary linear program and solving the primary linear program usi

Abstract: A method for obtaining an image of a sample having an external surface enclosing an inside, a light signal being emitted from within the inside, the method comprising: (a) providing two positioning images each comprising the external surface of the sample, (b) providing a light-emission image comprising data related to the light signal emitted from within the inside of the sample, (c) detecting a landmark pattern integral with the sample, (d) defining a transformation from the detected landmark position, (e) obtaining a referenced light-emission image by applying the transformation onto the light-emission image.

Abstract: A method for determining an optimal labeling of pixels in computer vision includes modeling an image by a graph having interior nodes and edges where each image point p is associated with a graph node, each pair of nearest neighbor points p, q is connected by a graph edge, each graph node p is associated with a singleton potential c(p), and each graph edge is associated with a pairwise potential function d(p,q). A label is randomly assigned to each point to initialize unary variables including an indicator function that indicates which label is assigned to which point and dual variables including height variables associated with each node p and label a, and balance variables associated with each edge (p,q) and label a. For each label, a new label c is selected, a capacitated graph is constructed and solved. The label selection divides the image into disjoint regions.

Abstract: A method for registering digitized images using Markov Random Fields (MRFs) includes providing a source image f and a target image g, defining a deformation grid of T ? ( x ) = x + ? p ? G ? ? ? ( ? x - p ? ) ? d u p , control points, defining a coordinate transformation as where x is a point on the source image, p is a position vector of control point p, dp is a displacement vector for each control point, up is a label for point p associated with displacement dp, and ?( ) is a weighting function for the displacement vector, defining an MRF energy functional to be minimized by T as E t = 1 ? G ? ? ? p ? G ? V p t ? ( u p ) + 1 ? E ? ? ? p , q ? E ? V pq ? ( u p , u q ) , wherein |G| is a number of control points, |E| is a number of pairs of neighboring control points on a neighborhood system, t is an iteration counter, and associating the MRF with a primary linear program and solving the primary linear program usin

Abstract: A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

Abstract: A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

Abstract: A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

Abstract: A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.