Medical　image　segmentation　commonly　involves　diverse　tissue　types　and　structures,　including　tasks　such　as　blood　vessel　segmentation　and　nerve　fiber　bundle　segmentation.　Enhancing　the　continuity　of　segmentation　outcomes　represents　a　pivotal　challenge　in　medical　image　segmentation,　driven　by　the　demands　of　clinical　applications,　focusing　on　disease　localization　and　quantification.　In　this　study,　a　novel　segmentation　model　is　specifically　designed　for　retinal　vessel　segmentation,　leveraging　vessel　orientation　information,　boundary　constraints,　and　continuity　constraints　to　improve　segmentation　accuracy.　To　achieve　this,　we　cascade　U-Net　with　a　long-short-term　memory　network　(LSTM).　U-Net　is　characterized　by　a　small　number　of　parameters　and　high　segmentation　efficiency,　while　LSTM　offers　a　parameter-sharing　capability.　Additionally,　we　introduce　an　orientation　information　enhancement　module　inserted　into　the　model＇s　bottom　layer　to　obtain　feature　maps　containing　orientation　information　through　an　orientation　convolution　operator.　Furthermore,　we　design　a　new　hybrid　loss　function　that　consists　of　connectivity　loss,　boundary　loss,　and　cross-entropy　loss.　Experimental　results　demonstrate　that　the　model　achieves　excellent　segmentation　outcomes　across　three　widely　recognized　retinal　vessel　segmentation　datasets,　CHASE_DB1,　DRIVE,　and　ARIA.