Alveolar nerve unfolding technique for synoptic analysis: visualization and quantification of inferior alveolar nerve tracings in three-dimensional cone-beam computed tomography.

The aim of the technique presented here is to visualize the anatomical context of the inferior alveolar nerve (IAN) canal. For 2 cases, cone-beam computed tomography images of the mandible were obtained from patient files together with the manual preoperative IAN canal tracings. For both cases, similar to simulated panoramic images, a two-dimensional image is extracted from a three-dimensional cone-beam computed tomography image. Unlike panoramic images, the unfolding does not follow the general curvature of the mandible but follows the nerve tracing closely and places the traced nerve track on a horizontal central line. Because of the centering of the nerve tracing together with the nerve canal and its surroundings in a two-dimensional representation, the technique (ANUTSA [Alveolar Nerve Unfolding Technique for Synoptic Analysis]) allowed the first case to evidence the adjacency of root tips along the IAN, whereas in the second case the degree of penetration of the IAN by an implant is revealed. The global aspect of the representation through unfolding allowed for the detection of the anomalies and the IAN-penetrating lesion along the IAN canal at a glance.

On the augmented reproducibility in measurements on 3D orthodontic digital dental models and the definition of feature points.

OBJECTIVE: The objective of this study was to explore digital measurement methodology on 3-dimensional (3D) dental models. Standardised manipulation and practices have lead to reliable measurements on plaster casts. Identifying landmarks on digital models or digitised plaster casts is fundamentally different from actual measurements. Three-dimensional models are represented on flat screens and landmarks are individually indentified. A procedure is proposed that resolves the deficiencies associated with a 2-dimensional (2D) display through an appropriate model representation and through local optimisation. METHODS: Fifteen models (OrthoProof) were exported to a locally developed 3D point indication software package, in which two measurement approaches were implemented involving standard projection with and without local search. Nine linear measurements were obtained from plaster casts and digital models. Statistical analysis included correlation and Friedman s nonparametric analysis of variance (ANOVA). RESULTS: For five out of nine linear measurements, digital indications yielded results significantly different from manual measurements (p = 0.05). Local search considerably improved measurement accuracy and reliability. CONCLUSIONS: Measurements on plaster casts can differ significantly from those obtained through digital identification methods. These differences prove to be clinically relevant. Standardisation and optimisation resulted in improved and extremely reliable digital measurements.

Vascular active contour for vessel tree segmentation.

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

Region competition based active contour for medical object extraction.

In this paper, a probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour. The algorithms are derived by minimizing a region based probabilistic energy function and implemented in a level set framework. An additional speed-controlling term makes the active contour quickly convergent to the actual contour on strong edges, whereas a probabilistic model makes the active contour performing well for weak edges. Prior knowledge about the initial contour and the probabilistic distribution contributes to more efficient extraction. The developed model has been applied to a variety of medical images, from CTA and MRA of the coronary to rotationally scanned and real-time three-dimensional echocardiography images of the mitral valve. As the results show, the algorithm is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.