Segmentation and spatial alignment of ultrasound (US) imaging data acquired in the in first trimester are crucial for monitoring human embryonic growth and development throughout this crucial period of life. Current approaches are either manual or semi-automatic and are therefore very time-consuming and prone to errors. To automate these tasks, we propose a multi-atlas framework for automatic segmentation and spatial alignment of the embryo using deep learning with minimal supervision. Our framework learns to register the embryo to an atlas, which consists of the US images acquired at a range of gestational age (GA), segmented and spatially aligned to a predefined standard orientation. From this, we can derive the segmentation of the embryo and put the embryo in standard orientation. US images acquired at 8+0 till 12+6 weeks GA were used and eight subjects were selected as atlas. We evaluated different fusion strategies to incorporate multiple atlases: 1) training the framework using atlas images from a single subject, 2) training the framework with data of all available atlases and 3) ensembling of the frameworks trained per subject. To evaluate the performance, we calculated the Dice score over the test set. We found that training the framework using all available atlases outperformed ensembling and gave similar results compared to the best of all frameworks trained on a single subject. Furthermore, we found that selecting images from the four atlases closest in GA out of all available atlases, regardless of the individual quality, gave the best results with a median Dice score of 0.72. We conclude that our framework can accurately segment and spatially align the embryo in first trimester 3D US images and is robust for the variation in quality that existed in the available atlases.

翻译：孕早期超声成像数据的分割与空间对齐对于监测这一关键生命时期的人类胚胎生长发育至关重要。现有方法多为手动或半自动方式，耗时且易出错。为实现这些任务的自动化，我们提出了一种基于多图谱框架的深度学习自动分割与空间对齐方法，仅需极少量人工监督。该框架学习将胚胎与图谱进行配准，图谱由覆盖不同孕周的超声图像组成，这些图像已被分割并空间对齐至预定义的标准方位。由此，我们可推导出胚胎的分割结果，并将其置于标准方位。研究使用了孕周8+0至12+6周的超声图像，并选取八名受试者作为图谱。我们评估了三种融合多图谱的策略：1）使用单个受试者的图谱图像训练框架，2）使用所有可用图谱数据训练框架，3）对单受试者训练的框架进行集成。通过测试集上的Dice系数评估性能后发现，使用所有可用图谱训练的框架优于集成方法，且结果与单受试者训练框架中的最佳表现相当。进一步分析表明，从所有图谱中筛选与目标孕周最接近的四个图谱（不考虑个体质量差异）可获得最优结果，其中位Dice系数为0.72。结论表明，本框架能准确实现孕早期三维超声图像中胚胎的分割与空间对齐，并对现有图谱中存在的质量差异具有良好的鲁棒性。