1. An atlas-free magnetic resonance imaging method configured for imaging at least one part of a brain, the method comprising the steps of: acquiring two image volumes using a magnetic resonance imaging sequence, respectively a first image volume and a second image volume, of the part of the brain at mutually different inversion times within a single acquisition, wherein the magnetic resonance imaging sequence is a Magnetization-Prepared 2 Rapid Gradient-Echoes sequence with a first echo time TE1 and a second echo time TE2 for each inversion contrast; acquiring a fat-water separated image of the part using a Dixon method, so that the fat and water component are in-phase and opposed-phase; acquiring, for each echo time TE1 and TE2, the two image volumes using magnetic resonance imaging and the magnetic resonance imaging sequence, respectively the first image volume and the second image volume at the first echo time, and the first image volume and the second image volume at the second echo time; combining the two image volumes obtained at the first echo time to form a first uniform image, and combining the two image volumes obtained at the second echo time to form a second uniform image; applying a fat-water separation method to the first image volume of the first and second inversion time TI1 and TI2 for obtaining a first fat-water separated image and/or applying the fat-water separation method to the second image volume of the first and second inversion time TI1 and TI2 for obtaining a second fat-water separated image; inputting a multichannel image segmentation algorithm with: a final uniform image resulting from a combination of the first uniform image at the first echo time and the second uniform image at the second echo time; a final fat-water separated image resulting from a combination of the fat-water separated images; a final second image volume resulting from a combination of the second image volumes; wherein the multichannel image segmentation algorithm uses a Markov random field model for segmenting the part into multiple classes of cranial tissues from data arising from the input images to enforce topological constraints without an atlas, wherein the topological constraints impose spatial relations between different tissues depending on tissue association; and outputting a segmented image of the part.