Mass　spectrometry　imaging　(MSI)　provides　valuable　insights　into　metabolic　heterogeneity　by　capturing　in　situ　molecular　profiles　within　organisms.　One　challenge　of　MSI　heterogeneity　analysis　is　performing　an　objective　segmentation　to　differentiate　the　biological　tissue　into　distinct　regions　with　unique　characteristics.　However,　current　methods　struggle　due　to　the　insufficient　incorporation　of　biological　context　and　high　computational　demand.　To　address　these　challenges,　a　novel　deep　learning-based　approach　is　proposed,　GraphMSI,　which　integrates　metabolic　profiles　with　spatial　information　to　enhance　MSI　data　analysis.　Our　comparative　results　demonstrate　GraphMSI　outperforms　commonly　used　segmentation　methods　in　both　visual　inspection　and　quantitative　evaluation.　Moreover,　GraphMSI　can　incorporate　partial　or　coarse　biological　contexts　to　improve　segmentation　results　and　enable　more　effective　three-dimensional　MSI　segmentation　with　reduced　computational　requirements.　These　are　facilitated　by　two　optional　enhanced　modes:　scribble-interactive　and　knowledge-transfer.　Numerous　results　demonstrate　the　robustness　of　these　two　modes,　ensuring　that　GraphMSI　consistently　retains　its　capability　to　identify　biologically　relevant　sub-regions　in　complex　practical　applications.　It　is　anticipated　that　GraphMSI　will　become　a　powerful　tool　for　spatial　heterogeneity　analysis　in　MSI　data.