The 4D Human Embryonic Brain Atlas: spatiotemporal atlas generation for rapid anatomical changes
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Abstract (arXiv)
Early brain development is crucial for lifelong neurodevelopmental health. However, current clinical practice offers limited knowledge of normal embryonic brain anatomy on ultrasound, despite the brain undergoing rapid changes within the time-span of days. To provide detailed insights into normal brain development and identify deviations, we created the 4D Human Embryonic Brain Atlas using a deep learning-based approach for groupwise registration and spatiotemporal atlas generation. Our method introduced a time-dependent initial atlas and penalized deviations from it, ensuring age-specific anatomy was maintained throughout rapid development. The atlas was generated and validated using 831 3D ultrasound images from 402 subjects in the Rotterdam Periconceptional Cohort, acquired between gestational weeks 8 and 12. We evaluated the effectiveness of our approach with an ablation study, which demonstrated that incorporating a time-dependent initial atlas and penalization produced anatomically accurate results. In contrast, omitting these adaptations led to anatomically incorrect atlas. Visual comparisons with an existing ex-vivo embryo atlas further confirmed the anatomical accuracy of our atlas. In conclusion, the proposed method successfully captures the rapid anotomical development of the embryonic brain. The resulting 4D Human Embryonic Brain Atlas provides a unique insights into this crucial early life period and holds the potential for improving the detection, prevention, and treatment of prenatal neurodevelopmental disorders.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 55 references that are in the map. First posted on arXiv 10 Mar 2025.
Builds on
4 of 55 references.
References · 4
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
- 2019Learning Conditional Deformable Templates with Convolutional Networkscited by 65
- 2022Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasoundcited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.