Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, United Kingdom.
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, United Kingdom; Visual Geometry Group, Department of Engineering Science, University of Oxford, United Kingdom.
Med Image Anal. 2018 May;46:1-14. doi: 10.1016/j.media.2018.02.006. Epub 2018 Feb 21.
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to address the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, we optimize the network by simultaneously learning features shared within the input data pertaining to the correlated tasks, and later branching out into task-specific output streams. Brain alignment is achieved by defining a parametric coordinate system based on skull boundaries, location of the eye sockets, and head pose, as predicted from intracranial structures. This information is used to estimate an affine transformation to align a volumetric image to the skull-based coordinate system. Co-alignment of 140 fetal ultrasound volumes (age range: 26.0 ± 4.4 weeks) was achieved with high brain overlap and low eye localization error, regardless of gestational age or head size. The automatically co-aligned volumes show good structural correspondence between fetal anatomies.
方法来对齐 3D 胎儿神经超声图像必须强大到 （i） 强度变化，（ii） 解剖和年龄特异性差异在胎儿人群中，以及 （iii） 胎儿位置的变化。为此，我们提出了一种多任务全卷积神经网络 （FCN） 架构来解决 3D 胎儿大脑定位、结构分割和对齐到参考坐标系的问题。我们不是将这些任务视为独立的问题，而是通过同时学习与相关任务相关的输入数据中的共享特征来优化网络，然后再扩展到特定于任务的输出流。通过基于颅骨边界、眼眶位置和头部姿势定义参数坐标系来实现脑对齐，这些信息是从颅内结构预测得到的。该信息用于估计仿射变换，将体积图像与基于颅骨的坐标系对齐。140 个胎儿超声体积 （年龄范围：26.0±4.4 周） 的共配准实现了高脑重叠和低眼定位误差，与胎龄或头部大小无关。自动共配准的体积显示出胎儿解剖结构之间的良好结构对应关系。
