Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.

Simultaneous frontal and lateral anatomic landmark identification improves three-dimensional localization reliability. Three-dimensional craniodental shape change from ages 8 to 18 within the Bolton standards presents little heterogeneity. Considerations of ethnicity aside, these may be initial grounds for use of these data as a normative referent.

We present a robust, fast and fully automatic approach enabling the segmentation of the main anatomical structures of the heart in CT images. The proposed method is based on the adaptation of a 3D triangulated mesh to new unknown images exploiting simultaneously knowledge of organ shape and typical gray level appearance in images, both learned from a training database made of 28 data sets. The described approach was tested on more than 50 volume images at different cardiac phases. Visual inspection by experts reveals that the proposed method is overall robust and succeeds in segmenting the heart up to minor interactive local corrections. IntroductionAutomatic segmentation plays a central role when inspecting reconstructed 3D cardiac images (e.g. from CT or MR scanners) [1]. An accurate classification of the different cardiac regions is usually the first step of subsequent tasks like: Visualization, coronary artery inspection, measurement of the ejection fraction for the left and right ventricles, wall motion analysis, intervention planning (e.g. for electro-physiology treatment).Because a significant amount of information about global functional analysis is delivered by the left ventricle, much work has been dedicated in extracting this structure from medical images. However, volumetric images acquired by emerging imaging techniques like e.g. multislice CT offer much more information, motivating the need for methods that are able to segment the other anatomical structures of the cardiac region.In this paper, we present a model-based approach capable of extracting the main anatomical structures of the heart. Unlike other works on model-based segmentation, we also concentrated our efforts in procedures enabling the initialization of the model without any click required from the user.During the training phase, typical boundary appearances are learned from representative images. This allows a very robust boundary detection when segmenting new unknown images. Moreover, although we adapt a single mesh, special attention has been paid in allowing each anatomical part of the model to be free to globally deform in an independent fashion. 2.Shape-constrained deformable models frameworkDeformable models have been widely used for the segmentation of medical images [2]. However, they might have too much flexibility when adapting towards the organ of interest and be sensitive to image artifacts. Using a priori information about shape variability to constrain the deformation flexibility has been recognized to improve the robustness of the segmentation process [3]. In this paper, we will use the approach introduced in [4], which constrains the model to remain close to the trained shape while allowing local non-learned deformations to account for the individuality of each patient.The input of the shape-constrained deformable model approach is a mesh with vertices v j (j = 0...V ) connected in T triangles. The mesh is adapted to a new image minimizing an energy function, which is usually made of two contributions. The fir...

Objective The treatment of craniofacial reconstructive surgery patients may benefit from comparison to average referent three-dimensional landmark data. These data may be useful for diagnosis, treatment planning, prosthetic design, or outcomes assessment. With regard to subadult patients, we hypothesize that the pattern of ontogenetic shape change of same sex, same ethnicity, referent populations will show gross uniformity. We present a preliminary shape analysis of 50 three-dimensional landmarks derived from 317 Bolton–Brush Growth Study biorthogonal image pairs. We determine which landmarks can be collected from scanned radiographs reliably by four operators for the precisely locatable points, ontogenetic trends in landmark configuration shape change, and patterns of sexual dimorphism. Participants Participants were Bolton standards individuals (16 male and 16 female) who contributed biplane cephalograms seven or more times with annual or greater spacing between ages 3 and 18 years. Design After removing outliers, we searched for ontogenetic heterogeneity, including sexual dimorphism and within sex-specific Procrustes coordinate shape spaces. Results A cut-off of 4.3-mm interoperator error left 32 landmarks in our analysis. Three different approaches (principal component analysis, age-trend analysis, and principal components of age residuals) all found no patterns of individual variation around sex-specific average trends of shape change. Male shape change peaks at age 15, a correlate of the growth spurt. Conclusions Simultaneous frontal and lateral anatomic landmark identification improves three-dimensional localization reliability. Three-dimensional craniodental shape change from ages 8 to 18 within the Bolton standards presents little heterogeneity. Considerations of ethnicity aside, these may be initial grounds for use of these data as a normative referent.