Abstract: A system and method automatically detect, in MR images, WM lesions exhibiting a central vein sign. A set of MR images of a brain lesion is acquired, using images of the set as input to different ML algorithms. A first ML algorithm classifies inputted image(s) into first or second classes. The first class includes CVS+/? and the second class CVSe lesions. A second ML algorithm classifies inputted image(s) into third or fourth classes. The third class includes CVS+ lesions and the fourth central vein sign? lesions. For each set, probability values are used that the set belongs to classes as inputs to a final classifier performing a final classification of the set into second, third, or fourth classes. For each class, the final classifier outputs final probability that the set belongs to the class. The second, third or fourth class with highest probability value is provided through an interface.

Abstract: A system and a method for mapping brain tissue damage from quantitative imaging data. The method is implemented by acquiring a quantitative map of a brain tissue parameter of said brain; acquiring a tractography map for said brain; superimposing a first map based on the quantitative map onto a second map based on the tractography map. Metrics are extracted from the superimposition that reflect a distribution of tract-specific quantitative values of the brain tissue parameter and the metrics of the brain are displayed.

Abstract: A system and a method measure volumetric changes of brain structures. The method includes initializing an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0. For all voxels that belong to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing the following steps: calculating a deviation of a volume change for the segmented brain structure from the longitudinal reference model, normalizing the deviation to obtain a quantitative value of the volume change on a same scale for voxel's belonging to different brain structures; and setting the intensity value of the voxels to the previously obtained quantitative value Q. The voxels of the 3D voxel dataset are displayed in a form of a longitudinal deviation map.

Abstract: A system and a method for mapping lesions or damage instances of a brain. The method includes receiving a lesion segmentation mask for the brain and receiving a tractography atlas. A connectivity damage brain map is constructed from (i) superimposing the lesion segmentation mask and a tractography atlas-based image, and (ii) combining information from the lesion segmentation mask with information from the tractography atlas-based image. The tractography atlas-based image is an image obtained from the tractography atlas, and the tractography atlas-based image and the lesion segmentation mask are registered to a common space.

Abstract: A system and a method measure volumetric changes of brain structures. The method includes initializing an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0. For all voxels that belong to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing the following steps: calculating a deviation of a volume change for the segmented brain structure from the longitudinal reference model, normalizing the deviation to obtain a quantitative value of the volume change on a same scale for voxel's belonging to different brain structures; and setting the intensity value of the voxels to the previously obtained quantitative value Q. The voxels of the 3D voxel dataset are displayed in a form of a longitudinal deviation map.

Abstract: A system and method automatically predicts an evolution of a cognitive score for a subject by classifying a cognitive data set for the subject into a first or second class by determining the cognitive set of data for each subject of a group, acquiring for each subject a neuropsychological score used for classifying each subject in the first or second class, and training a two-class machine learning classification algorithm on the cognitive data sets of all subjects. For each subject, the cognitive data set is used as input of the algorithm and the obtained classification of the subject as output target of the algorithm. The algorithm classifies each cognitive data set in the first or second class. The evolution of the cognitive score of a subject is predicted by the trained algorithm for automatically classifying a new cognitive dataset for the subject into the first or second class.

Abstract: System and method for automatically predicting an evolution of a cognitive score for a subject by classifying a cognitive data set for said subject into either a first class or a second class, the method comprising: determining the cognitive set of data for each subject of a group of subjects; acquiring for each subject a CDRSoB neuropsychological score, wherein said CDRSoB neuropsychological score is used for classifying each subject either in the first class or in the second class; training a two-class machine learning classification algorithm on the cognitive data sets of all subjects, wherein for each subject, the cognitive data set is used as input of the algorithm and the obtained classification of the subject as output target of the algorithm, wherein the two-class machine learning classification algorithm is configured for classifying each cognitive data set either in said first class or in said second class; predicting the evolution of the cognitive score of a subject by using the trained two-clas