Retinal　vessel　segmentation　is　a　critical　step　in　computer-aided　diagnosis　of　retinal　images,　providing　rich　information　of　vascular　morphology.　However,　existing　works　primarily　focus　on　achieving　high　overall　segmentation　accuracy,　often　overlooking　the　errors　in　the　topological　structure　that　lead　to　disconnected　vessels.　In　order　to　address　these　issues,　a　topological　coherence　preserving　retinal　vessel　segmentation　method　is　proposed.　We　adopt　a　two-stage　model　by　cascading　a　refinement　step　after　the　typical　segmentation　network　to　enhance　retinal　vessel　connectivity.　A　topology-preserving　loss　is　then　introduced　to　guide　the　refinement　network　to　pay　more　attention　to　key　regions　with　topological　differences,　allowing　it　to　learn　the　correct　topology　structure　and　maximize　the　preservation　of　retinal　vessel　tree　connectivity.　Experimental　results　on　the　DRIVE,　STARE,　and　CHASE_DB1　datasets　demonstrate　the　effectiveness　of　the　proposed　method.　©　2023　IEEE.