  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.
