title: Self-supervised MRI tissue segmentation by discriminative clustering.
abstract: The study of brain lesions can benefit from a clear identification of transitions between healthy and pathological tissues, through the analysis of brain imaging data. Current signal processing methods, able to address these issues, often rely on strong prior information. In this article, a new method for tissue segmentation is proposed. It is based on a discriminative strategy, in a self-supervised machine learning approach. This method avoids the use of prior information, which makes it very versatile, and able to cope with different tissue types. It also returns tissue probabilities for each voxel, crucial for a good characterization of the evolution of brain lesions. Simulated as well as real benchmark data were used to validate the accuracy of the method and compare it against other segmentation algorithms.
authors.affiliation: Department of Information and Computer Science, Aalto University School of Science, P. O. Box 15400, FI-00076 Aalto, Espoo, Finland.
authors.forename: Nicolau
authors.initials: N
authors.lastname: Gonçalves
authors.forename: Janne
authors.initials: J
authors.lastname: Nikkilä
authors.forename: Ricardo
authors.initials: R
authors.lastname: Vigário
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001930:Brain Injuries / Q000175:diagnosis*; D001931:Brain Mapping; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated