Abstract: A device and a method for training a machine learning model for detecting the presence of at least one region of interest in a 3D image of a subject. Also, a device and a method for detecting and reconstructing at least one region of interest in a 3D image of a subject using a trained machine learning model obtained with the method and device for training.

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: A computer-implemented method for generating a registered image (Breg) based on at least one first image of an object compliant with a source imaging modality and on at least one second image of the object compliant with a target imaging modality. The object may include a human body part, and the structures of interest may include stiff regions such as bones, cartilage, or tendons.

Abstract: A computer-implemented method for reconstructing a 3D image of a human body part studied of a subject from at least one 2D projection of the human body part studied of the subject and a generative network trained for reconstructing 3D images. The human body part may be the head and/or the neck.

Abstract: A method for generating a 3D image of a human body part including: train a generative network based on a library of human body part 3D scans of reference to obtain a generative model; make a 3D scan of a studied human body part; define a subset of the studied 3D scan by excluding the content of an area; optimize a latent variable for minimizing a distance between the defined subset and an image of a subset generated by the generative model from the latent variable; generate a complete 3D image of the studied human body part with the generative model using the optimized latent variable.

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: 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.