MRI with hyperpolarized 13C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of metabolism requires a region-of-interest to be defined, which is usually based on spatial information only. However, as the hyperpolarized data is 5-dimensional (spatial, temporal and spectral), it offers the possibility of applying novel segmentation methods to more accurately define this region-of-interest. A novel solution to the problem of 13C image segmentation is proposed here, using a hybrid Markov random field model with fuzzy logic. Performance of the algorithm is demonstrated using in silico and in vivo data.