Refined　and　automatic　retinal　vessel　segmentation　is　crucial　for　computer-aided　early　diagnosis　of　retinopathy.　However,　existing　methods　often　suffer　from　mis-segmentation　when　dealing　with　thin　and　low-contrast　vessels.　In　this　paper,　a　two-path　retinal　vessel　segmentation　network　is　proposed,　namely　TP-Net,　which　consists　of　three　core　parts,　i.e.　main-path,　sub-path,　and　multi-scale　feature　aggregation　module　(MFAM).　Main-path　is　to　detect　the　trunk　area　of　the　retinal　vessels,　and　the　sub-path　to　effectively　capture　edge　information　of　the　retinal　vessels.　The　prediction　results　of　the　two　paths　are　combined　by　MFAM,　obtaining　refined　segmentation　of　retinal　vessels.　In　the　main-path,　a　three-layer　lightweight　backbone　network　is　elaborately　designed　according　to　the　characteristics　of　retinal　vessels,　and　then　a　global　feature　selection　mechanism　(GFSM)　is　proposed,　which　can　autonomously　select　features　that　are　more　important　for　the　segmentation　task　from　the　features　at　different　layers　of　the　network,　thereby,　enhancing　the　segmentation　capability　for　low-contrast　vessels.　In　the　sub-path,　an　edge　feature　extraction　method　and　an　edge　loss　function　are　proposed,　which　can　enhance　the　ability　of　the　network　to　capture　edge　information　and　reduce　the　mis-segmentation　of　thin　vessels.　Finally,　MFAM　is　proposed　to　fuse　the　prediction　results　of　main-path　and　sub-path,　which　can　remove　background　noises　while　preserving　edge　details,　and　thus,　obtaining　refined　segmentation　of　retinal　vessels.　The　proposed　TP-Net　has　been　evaluated　on　three　public　retinal　vessel　datasets,　namely　DRIVE,　STARE,　and　CHASE　DB1.　The　experimental　results　show　that　the　TP-Net　achieved　a　superior　performance　and　generalization　ability　with　fewer　model　parameters　compared　with　the　state-of-the-art　methods.　IEEE