Abstract
We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. We flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy density to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of the placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .
Abstract (translated)
我们提出了一种基于体积网格的胎盘扁平化算法，以实现局部解剖和功能的有效可视化。在体内监测胎盘功能可支持妊娠评估并改善护理效果。我们的目的是减轻胎盘附着于弯曲子宫壁时胎盘形状所带来的视觉和解释挑战。我们将捕获胎盘形状的体积网格展平，使其与研究良好的体外形状相似。我们将我们的方法描述为从活体形状到扁平模板的映射，该模板最小化对称Dirichlet能量密度，以控制整个体积的变形。在梯度下降过程中，通过约束线搜索实现局部注入。我们在胎盘功能研究中评估了从MRI图像中提取的28个胎盘形状的方法。我们在将胎盘边界映射到模板的同时成功地控制整个容积的畸变，从而实现了子体素的精确性。我们说明了由此产生的胎盘定位如何增强胎盘解剖和功能的可视化。我们的代码可以在https://github.com/mabulnaga/胎盘压平上免费获得。
URL
https://arxiv.org/abs/1903.05044
