Academic radiology
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Clinical Trial
Smoothing lung segmentation surfaces in three-dimensional X-ray CT images using anatomic guidance.
Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum. ⋯ We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Because there are no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for three-dimensional smoothing.
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Interventional magnetic resonance imaging (iMRI) allows real-time guidance and optimization of radiofrequency ablation of pathologic tissue. For many tissues, resulting lesions have a characteristic two-boundary appearance featuring an inner region and an outer hyper-intense margin in both T2 and contrast-enhanced (CE) T1-weighted MR images. We created a geometric model-based semiautomatic method to aid in real-time lesion segmentation, cross-sectional/three-dimensional visualization, and intra/posttreatment evaluation. ⋯ Our method provides a precise, semiautomatic approximation of lesion shape for ellipsoidal lesions. Further, the method has clinical applications in lesion visualization, volume estimation, and treatment evaluation.