Abstract

Segmentation and spatial alignment of ultrasound imaging data acquired in the in first trimester are crucial for monitoring early 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 ultrasound images acquired at a range of gestational ages, 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. Ultrasound images acquired at 8+0 till 12+6 weeks gestational age were used and eight pregnancies were selected as atlas images. 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 gestational age 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 ultrasound images and is robust for the variation in quality that existed in the available atlases. Our code is publicly available at: https://gitlab.com/radiology/prenatal-image-analysis/multi-atlas-seg-reg