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SEGMENTATION OF ELASTOGRAPHIC IMAGES USING A COARSE-TO-FINE ACTIVE CONTOUR MODEL

机译:运用精细到精细的主动轮廓模型分割弹性图像

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摘要

Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.
机译:射频消融引起的凝结(热损伤)边界的划定是重要的临床问题,传统的成像方式无法很好地解决这一问题。弹性成像可以在较小的外部施加压缩后产生局部应变的图像,可用于可视化热凝。本文提出了一种自动分割方法,可对3-D弹性成像数据进行热凝,以快速获取面积和体积信息。该方法由用于主动轮廓初始化的粗到精方法和梯度矢量流,用于可变形轮廓优化的主动轮廓模型组成,借助于对一般热凝结几何学的先验知识。该算法的性能已证明与医学物理学家在弹性图上手动描绘凝结物相当(对于36次射频诱导的凝结物,r = 0.99)。此外,应用于弹性图的自动算法产生的结果与在病理图像上手动描绘凝血的结果相符(对于相同的36个病变,r = 0.96)。该算法也已成功应用于体内弹性图。

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