Extracting branching tubular object geometry via cores.

Blood vessels and other anatomic objects in the human body can be described as trees of branching tubes. The focus of this paper is the extraction of the branching geometry in three-dimensional, as well as the extraction of the tubes themselves, via skeletons computed as cores. Cores are height ridges of a graded measure of medial strength called medialness, which measures how much a given location resembles the middle of an object as indicated by image intensities. Object bifurcations are detected using an affine-invariant corner detector and computations on the core's medialness values. The methods presented in this paper are evaluated on synthetic images of branching tubular objects as well as on blood vessels in head MR angiogram data. Results show impressive resistance to noise and the ability to detect branches spanning a variety of widths and branching angles. An extension that allows cores to extract general branching structures, not only branching tubes, is introduced.

Deformable M-Reps for 3D Medical Image Segmentation.

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.

DNA bending and unbending by MutS govern mismatch recognition and specificity.

DNA mismatch repair is central to the maintenance of genomic stability. It is initiated by the recognition of base-base mismatches and insertion/deletion loops by the family of MutS proteins. Subsequently, ATP induces a unique conformational change in the MutS-mismatch complex but not in the MutS-homoduplex complex that sets off the cascade of events that leads to repair. To gain insight into the mechanism by which MutS discriminates between mismatch and homoduplex DNA, we have examined the conformations of specific and nonspecific MutS-DNA complexes by using atomic force microscopy. Interestingly, MutS-DNA complexes exhibit a single population of conformations, in which the DNA is bent at homoduplex sites, but two populations of conformations, bent and unbent, at mismatch sites. These results suggest that the specific recognition complex is one in which the DNA is unbent. Combining our results with existing biochemical and crystallographic data leads us to propose that MutS: (i) binds to DNA nonspecifically and bends it in search of a mismatch; (ii) on specific recognition of a mismatch, undergoes a conformational change to an initial recognition complex in which the DNA is kinked, with interactions similar to those in the published crystal structures; and (iii) finally undergoes a further conformational change to the ultimate recognition complex in which the DNA is unbent. Our results provide a structural explanation for the long-standing question of how MutS achieves mismatch repair specificity.