Automatic estimation of local vascular measurements, such as center-ness, radius, orientation, and vascular mask, could provide quantitative analysis of vascular diseases and support surgical procedures in clinical applications. However, developing a single model for vascular image computing poses additional challenges because vessels are widely spread but have thin and network-like structures. Traditionally, tubularity has been employed as the primary assumption to enhance the vascular features from images. Existing deep-learning-based approaches incorporate the assumptions of tubular structures in an implicit manner. This, however, imposes limitations on the potential for extensive exploration in the realm of effective feature extraction. In this paper, we propose a threefold strategy. First, we design a combined computing framework to estimate various local measurements of vessels. Second, we propose a tubular shape-guided convolution, i.e., orientational-deformable convolution, where the cuboid grids are rotated and deformed according to vascular orientations to capture the vascular features efficiently. Third, we deploy a tubular sampling strategy within the network to pinpoint the vascular center-ness accurately. Experimental results conducted on two vascular datasets containing coronary and cerebral vessels demonstrate that the method exhibits superior accuracy, particularly in identifying middle and distal vessels.