Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Abstract-We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thin-plate splines are special cases of this scheme. To localize landmarks we use a semi-automatic approach which is based on three-dimensional (3-D) differential operators. Experimental results are presented for two-dimensional as well as 3-D tomographic images of the human brain.

Under mechanical loading, most living cells show a viscoelastic deformation that follows a power law in time. After removal of the mechanical load, the cell shape recovers only incompletely to its original undeformed configuration. Here, we show that incomplete shape recovery is due to an additive plastic deformation that displays the same power-law dynamics as the fully reversible viscoelastic deformation response. Moreover, the plastic deformation is a constant fraction of the total cell deformation and originates from bond ruptures within the cytoskeleton. A simple extension of the prevailing viscoelastic power-law response theory with a plastic element correctly predicts the cell behaviour under cyclic loading. Our findings show that plastic energy dissipation during cell deformation is tightly linked to elastic cytoskeletal stresses, which suggests the existence of an adaptive mechanism that protects the cell against mechanical damage.

Abstract. We consider elastic registration of medical image data based on thin-plate splines using a set of corresponding anatomical point landmarks. Previous work on this topic has concentrated on using interpolation schemes. Such s c hemes force the corresponding landmarks to exactly match each other and assume that the landmark positions are known exactly. H o wever, in real applications the localization of landmarks is always prone to some error. Therefore, to take i n to account these localization errors, we h a ve i n vestigated the application of an approximation scheme which is based on regularization theory. This approach generally leads to a more accurate and robust registration result. In particular, outliers do not disturb the registration result as much a s i s t h e c a s e with an interpolation scheme. Also, it is possible to individually weight the landmarks according to their localization uncertainty. In addition to this study, w e report on investigations into semi-automatic extraction of anatomical point landmarks.

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