Automatic　segmentation　and　centerline　extraction　of　blood　vessels　from　retinal　fundus　images　is　an　essential　step　to　measure　the　state　of　retinal　blood　vessels　and　achieve　the　goal　of　auxiliary　diagnosis.　Combining　the　information　of　blood　vessel　segments　and　centerline　can　help　improve　the　continuity　of　results　and　performance.　However,　previous　studies　have　usually　treated　these　two　tasks　as　separate　research　topics.　Therefore,　we　propose　a　novel　multitask　learning　network　(MSC-Net)　for　retinal　vessel　segmentation　and　centerline　extraction.　The　network　uses　a　multibranch　design　to　combine　information　between　two　tasks.　Channel　and　atrous　spatial　fusion　block　(CAS-FB)　is　designed　to　fuse　and　correct　the　features　of　different　branches　and　different　scales.　The　clDice　loss　function　is　also　used　to　constrain　the　topological　continuity　of　blood　vessel　segments　and　centerline.　Experimental　results　on　different　fundus　blood　vessel　datasets　(DRIVE,　STARE,　and　CHASE)　show　that　our　method　can　obtain　better　segmentation　and　centerline　extraction　results　at　different　scales　and　has　better　topological　continuity　than　state-of-the-art　methods.