1. A method for segmenting an organ in n-channel volume data records from magnetic resonance imaging, wherein n≧2, the method comprising: generating probability distributions on the basis of segmentation results from n-channel training data records, the probability distributions specifying at least probabilities of positions of voxels and intensity values, which have been reduced from n-dimensions to one dimension with the aid of discriminant reduction, of voxels in the training data records belonging to the organ; generating at least one 3D probability data record from the n-channel volume data records on the basis of the probability distributions, wherein in the generated at least one 3D probability data record, each voxel is assigned a probability of belonging to the organ; setting a maximum value of the at least one 3D probability data record as a start point for the segmentation; and segmenting the organ on the basis of the at least one 3D probability data record using a region-growing technique, wherein a multistage segmentation of a liver, as the organ, is performed, wherein, after a first segmentation pass, there at least is a refinement at a transition between the liver and a kidney on the basis of a known anatomy of liver and the kidney in order to reduce or avoid oversegmentation in this region, and wherein the probability distributions comprise 3-class probability distributions that specify probabilities of positions of voxels and intensity values, which have been reduced from n-dimensions to one dimension with the aid of discriminant reduction, of voxels in the training data records belonging to the liver, the kidney or the background, and wherein at least the following are performed for determining the 3-class probabilities: providing segmentation results that comprise n-dimensional vector voxels of the liver, n-dimensional vector voxels of the kidney and n-dimensional vector voxels of the background; performing linear discriminant reduction such that an n-dimensional first projection vector is obtained; projecting the vector voxels of the liver onto the first projection vector in order to obtain first intensity values reduced to one dimension of the voxels of the liver; projecting the vector voxels of the kidney onto the first projection vector in order to obtain first intensity values reduced to one dimension of the voxels of the kidney; projecting the vector voxels of the background onto the first projection vector in order to obtain first intensity values reduced to one dimension of the voxels of the background; and calculating the 3-class probability distributions from the first intensity values reduced to one dimension.