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… Show more
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