Atherosclerotic　plaques,　the　leading　cause　of　heart　attack,　can　be　characterized　from　intravascular　optical　coherence　tomography　(IV-OCT)　images　by　doctors.　Since　lipid　accumulation　is　an　important　indication　of　atherosclerotic　plaque,　we　introduced　a　new　convolutional　neural　network,　called　Single　Shot　Plaque　Marking　Network　(SSPM),　to　develop　an　automated　method　that　highlights　the　extent　of　lipid　plaques　from　IV-OCT　images　at　real-time,　which　then　would　help　doctors　easily　find　the　vulnerable　plaque.　Compared　with　previous　available　methods,　our　method　is　capable　of　marking　the　suspicious　lipid　plaque　areas　in　real-time　with　better　time-efficiency　and　competitive　accuracy　during　the　diagnosis.　SSPM　is　tested　on　IV-OCT　human　coronary　artery　imaging　dataset,　and　the　result　shows　that　our　method　is　able　to　mark　suspicious　lipid-plaque　areas　at　91　fps　on　GPU,　or　16　fps　on　CPU,　with　an　accuracy　of　87%.　2019,　Springer　Nature　Singapore　Pte　Ltd.