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.

No abstract

No abstract

Abstract. We present a fully automatic segmentation algorithm for the whole heart (four chambers, left ventricular myocardium and trunks of the aorta, the pulmonary artery and the pulmonary veins) in cardiac MR image volumes with nearly isotropic voxel resolution, based on shape-constrained deformable models. After automatic model initialization and reorientation to the cardiac axes, we apply a multi-stage adaptation scheme with progressively increasing degrees of freedom. Particular attention is paid to the calibration of the MR image intensities. Detailed evaluation results for the various anatomical heart regions are presented on a database of 42 patients. On calibrated images, we obtain an average segmentation error of 0.76mm.

Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3) approximately 2.1 mm compared with registered intra-procedural EAM point clouds. The overall experiments indicate that LAPV surfaces can be automatically segmented intra-procedurally from 3-D RA data with comparable quality relative to meshes derived from pre-procedural CT/MR.

No abstract

Background— There is no systematic assessment of available evidence on effectiveness and comparative effectiveness of balloon dilatation and stenting for aortic coarctation. Methods and Results— We systematically searched 4 online databases to identify and select relevant studies of balloon dilatation and stenting for aortic coarctation based on a priori criteria (PROSPERO 2014:CRD42014014418). We quantitatively synthesized results for each intervention from single-arm studies and obtained pooled estimates for relative effectiveness from pairwise and network meta-analysis of comparative studies. Our primary analysis included 15 stenting (423 participants) and 12 balloon dilatation studies (361 participants), including patients ≥10 years of age. Post-treatment blood pressure gradient reduction to ≤20 and ≤10 mm Hg was achieved in 89.5% (95% confidence interval, 83.7–95.3) and 66.5% (44.1–88.9%) of patients undergoing balloon dilatation, and in 99.5% (97.5–100.0%) and 93.8% (88.5–99.1%) of patients undergoing stenting, respectively. Odds of achieving ≤20 mm Hg were lower with balloon dilatation as compared with stenting (odds ratio, 0.105 [0.010–0.886]). Thirty-day survival rates were comparable. Numerically more patients undergoing balloon dilatation experienced severe complications during admission (6.4% [2.6–10.2%]) compared with stenting (2.6% [0.5–4.7%]). This was supported by meta-analysis of head-to-head studies (odds ratio, 9.617 [2.654–34.845]) and network meta-analysis (odds ratio, 16.23, 95% credible interval: 4.27–62.77) in a secondary analysis in patients ≥1 month of age, including 57 stenting (3397 participants) and 62 balloon dilatation studies (4331 participants). Conclusions— Despite the limitations of the evidence base consisting predominantly of single-arm studies, our review indicates that stenting achieves superior immediate relief of a relevant pressure gradient compared with balloon dilatation.

Segmentation of organs in medical images can be successfully performed with shape-constrained deformable models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be automatically segmented using a suitable shape variablility model based on piecewise affine degrees of freedom.In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers, the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments.On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of 0.61-0.80 mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels, surface accuracies of 0.47-1.17 mm are obtained (larger errors are asscociated with faintly contrasted venous structures).