There is growing interest in the rich temporal and spectral properties of the functional connectome of the brain that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from the cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of regions of interest (ROIs) while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions of data from 102 magnetometers, 204 planar gradiometers and 70 EEG sensors, both algorithms yielded approximately 70 ROIs despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, we found significant improvements in both sensitivity and distinguishability of the ROIs. Furthermore, by simulating a realistic connectome with a single hub, we show that the choice of parcellation can have significant impact on the outcome of graph theoretical analysis of the source-reconstructed EEG/MEG. Thus, CTF-informed, adaptive parcellations allow a more accurate reconstruction of functional connectomes from EEG/MEG data.