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首页> 外文会议>Computational Imaging V; Proceedings of SPIE-The International Society for Optical Engineering; vol.6498; Electronic Imaging Science and Technology >4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors
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4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors

机译：使用具有时空形状先验的活动表面对心脏数据进行4D分割

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摘要

We present a 4D spatiotemporal segmentation algorithm based on the Mumford-Shah functional coupled with shape priors. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquired data to manually retrieve diagnostically meaningful measurements. The advantage of the 4D algorithm is that segmentation occurs in both space and time simultaneously, improving accuracy and robustness over existing 2D and 3D methods. The segmentation contour or hyper-surface is a zero level set function in 4D space that exploits the coherence within continuous regions not only between spatial slices, but between consecutive time samples as well. Shape priors are incorporated into the segmentation to limit the result to a known shape. Variations in shape are computed using principal component analysis (PCA), of a signed distance representation of the training data derived from manual segmentation of 18 carefully selected data sets. The automatic segmentation occurs by manipulating the parameters of this signed distance representation to minimize a predetermined energy functional. Several tests are presented to show the consistency and accuracy of the novel automatic 4D segmentation process.
机译：我们提出了一种基于Mumford-Shah函数并结合形状先验的4D时空分割算法。当在临床环境中使用时，我们的算法可以大大减少临床医生必须花费的时间处理采集到的数据以手动检索具有诊断意义的测量结果的时间。 4D算法的优势在于，分割同时在空间和时间上进行，与现有的2D和3D方法相比，提高了准确性和鲁棒性。分割轮廓或超曲面是4D空间中的零级集函数，它不仅利用空间切片之间的连续区域内的连续性，还利用连续时间样本之间的连续性。形状先验被合并到分割中以将结果限制为已知形状。使用主成分分析（PCA），可以对18个经过精心挑选的数据集进行人工分割而得到的训练数据的带符号距离表示形式进行形状变化计算。通过操纵该带符号的距离表示的参数以最小化预定的能量功能来进行自动分割。提出了一些测试，以显示新颖的自动4D分割过程的一致性和准确性。

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