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首页> 外文期刊>Medical image analysis >Tunneling descent for m.a.p. active contours in ultrasound segmentation.
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Tunneling descent for m.a.p. active contours in ultrasound segmentation.

机译：m.a.p.的隧道下降超声分割中的活动轮廓。

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摘要

Active contours that evolve in ultrasound images under gradient descent are often trapped in spurious local minima. This paper presents an evolution strategy called tunneling descent, which is capable of escaping from such minima. The key idea is to evolve the contour by a sequence of constrained minimizations that move the contour in to, and out of, local minima. This strategy is an extension of classical gradient descent. Because tunneling descent does not terminate at a local minima an explicit stopping rule is required. Model-based and model-free stopping rules are presented and formulae for choosing the stopping threshold are given. The algorithm is used to segment the endocardium in 44 short axis cardiac ultrasound images. The energy function of the active contour is derived from a m.a.p. formulation. All segmentations are achieved without tweaking either the energy function or numerical parameters. Experimental evaluation of the segmentations show that the algorithm overcomes multiple local minima to find the endocardium. The accuracy of the algorithm is comparable to that of manual segmentations and significantly better than classical gradient descent active contours. The sensitivity of the segmentation to initialization is also evaluated and it is shown that segmentations from quite different initializations are close to each other. Finally, some limitations of the m.a.p. formulation are discussed.
机译：在梯度下降下在超声图像中演化的活动轮廓通常被困在虚假的局部最小值中。本文提出了一种进化策略，称为隧道下降，它能够逃脱这种最小值。关键思想是通过一系列将轮廓移入和移出局部最小值的约束最小化来演变轮廓。此策略是经典梯度下降的扩展。由于隧道下降未在局部最小值处终止，因此需要明确的停止规则。提出了基于模型和无模型的停车规则，给出了停车阈值的选择公式。该算法用于在44个短轴心脏超声图像中分割心内膜。有效轮廓的能量函数从m.a.p.公式。无需调整能量函数或数值参数即可实现所有分割。分割的实验评估表明，该算法克服了多个局部最小值，从而找到了心内膜。该算法的精度与手动分割的精度相当，并且明显优于经典的梯度下降主动轮廓。还评估了分段对初始化的敏感性，并且显示出来自完全不同的初始化的分段彼此接近。最后，m.a.p。的一些限制讨论配方。

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