The　normal　functioning　of　human　organs　and　tissues　depends　on　the　spatial　organization,　specialization,　and　interaction　between　cells.　With　a　total　of　37　trillion　cells　in　the　human　body,　determining　their　functions　and　relationships　is　highly　challenging.　The　Vascular　Coordinate　Common　Framework　(VCCF)　is　widely　used　for　cell　mapping,　but　it　is　limited　by　a　lack　of　knowledge　about　the　microvascular　system.　Therefore,　we　propose　a　novel　framework　called　Attention-based　Mask　R-CNN　for　Microvascular　Segmentation　to　achieve　automated　segmentation　of　microvascular　arrangements.　In　terms　of　structure,　our　model　replaces　the　original　fully　connected　layers　with　the　Feature　Pyramid　Network　(FPN)　as　the　segmentation　head　network　and　integrates　feature　maps　from　different　levels.　To　enhance　performance,　we　introduce　RoI　(Region　of　Interest)　feature　alignment,　employing　techniques　such　as　bilinear　interpolation　to　accurately　align　features　within　the　RoI　region.　Furthermore,　to　better　focus　on　the　regions　of　interest,　we　introduce　an　attention　mechanism　specifically　designed　for　Human　Vasculature,　which　improves　the　segmentation　accuracy　of　critical　areas.　We　train　our　model　using　2D　PAS-stained　histological　images　of　healthy　human　kidney　tissue　sections.　Experimental　results　demonstrate　the　superior　segmentation　outcomes　of　our　approach　for　microvascular　structures,　including　capillaries,　arterioles,　and　venules.　©　2023　IEEE.