Obtaining　pixel-accurate　and　topologically　complete　retinal　vessel　segmentation　is　challenging　due　to　many　factors,　for　instance,　vascular　structure　complexity,　image　contrast　variations,　and　limitations　of　valuable　datasets.　In　this　paper,　we　introduce　a　novel　network　structure　that　applies　dual-branch:　directional　reweighted　branch　and　skeletonized　branch.　In　the　direction　reweighted　branch,　we　propose　adaptive　directional　enhancement　and　connectivity　consistency　enhancement,　which　can　be　used　to　extract　favorable　directional　channel　information　and　model　the　bidirectional　relationship　between　pixels,　respectively.　In　the　skeletonized　branch,　we　employ　morphological　skeletonization　to　align　the　ground　truth　with　the　predicted　segmentation　map.　By　doing　that,　we　effectively　preserve　the　vessel＇s　topological　structure　from　a　global　perspective.　Extensive　experiments　on　publicly　available　retinal　datasets　DRIVE,　CHASE_DB1,　and　STARE　show　that　our　proposed　approach　has　achieved　significant　results　in　preserving　vessel　structure　and　accurate　segmentation.　©　2024　IEEE.