Ucbbiopharma Srl

Joeri Nicolaes

Andrew Robert Carnell

A machine-based learning system for predicting the presence of lesions indicative of axial spondyloarthritis in medical imaging data includes an image processor for receiving and processing one or more MRI image data set containing pelvic region and sacroiliac joint of a subject, and dividing said MRI image data set into a set of 3D voxels. The system also includes a voxel classifier for calculating for each of said the voxels one or more class probabilities of such voxel to contain a lesion of a particular type or being a non-lesion. The system also includes a lesion probability map generator for receiving the data produced by the voxel classifier and producing a probability intensity map for each of lesion types, in which one or more areas classified as lesion are highlighted; and an image display for displaying the output of the lesion probability map generator.

Medical image analysis system and method for identification of lesions

A machine-based learning method estimates a probability of bone fractures in a 3D image, more specifically vertebral fractures. The method and system utilizing such method utilize a data-driven computational model to learn 3D image features for classifying vertebra fractures. A three-dimensional medical image analysis system for predicting a presence of a vertebral fracture in a subject includes a 3D image processor for receiving and processing 3D image data of a 3D image of the subject, producing two or more sets of 3D voxels. Each of the sets of 3D voxels corresponds to an entirety of the 3D image and each of the sets of 3D voxels consists of equal 3D voxels of different dimensions. The system also includes a voxel classifier for assigning the 3D voxels one or more class probabilities each of the 3D voxels contains a fracture using a computational model, and a fracture probability estimator for estimating a probability of the presence of a vertebral fracture in the subject.

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Brussels, Belgium

Joeri Nicolaes