The simulation of pituitary gland surgery requires a precise classification of soft tissues, vessels and bones. Bone structures tend to be thin and have diffuse edges in CT data, and thus the common method of thresholding can produce incomplete segmentations. In this paper, we present a novel multi-scale sheet enhancement measure and apply it to paranasal sinus bone segmentation. The measure uses local shape information obtained from an eigenvalue decomposition of the Hessian matrix. It attains a maximum in the middle of a sheet, and also provides local estimates of its width and orientation. These estimates are used to create a vector field orthogonal to bone boundaries, so that a flux maximizing flow algorithm can be applied to recover them. Hence, the sheetness measure has the essential properties to be incorporated into the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We validate the approach quantitatively on synthetic examples, and provide comparisons with existing segmentation techniques on paranasal sinus CT data.