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Continuous Longitudinal Fetus Brain Atlas Construction via Implicit Neural Representation

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Perinatal, Preterm and Paediatric Image Analysis (PIPPI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13575))

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Abstract

Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time inconsistency issue as a 4D (3D brain volume + 1D age) image data denoising task. Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate. Experimental results on two public fetal brain atlases (CRL and FBA-Chinese atlases) show that the proposed method can significantly improve the atlas temporal consistency while maintaining good fetus brain structure representation. In addition, the continuous longitudinal fetus brain atlases can also be extensively applied to generate finer 4D atlases in both spatial and temporal resolution.

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Authors and Affiliations

  1. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

    Lixuan Chen, Jiangjie Wu, Qing Wu & Yuyao Zhang

  2. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Hongjiang Wei

  3. Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, Shanghai, China

    Yuyao Zhang

Authors
  1. Lixuan Chen
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  2. Jiangjie Wu
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  3. Qing Wu
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  4. Hongjiang Wei
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  5. Yuyao Zhang
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Corresponding author

Correspondence to Yuyao Zhang .

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Editors and Affiliations

  1. Massachusetts General Hospital & Harvard Medical School, Charlestown, MA, USA

    Roxane Licandro

  2. King’s College London, London, UK

    Andrew Melbourne

  3. Children’s Hospital, Boston, MA, USA

    Esra Abaci Turk

  4. The Hospital for Sick Children Research Institute, Toronto, ON, Canada

    Christopher Macgowan

  5. King’s College London, London, UK

    Jana Hutter

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Chen, L., Wu, J., Wu, Q., Wei, H., Zhang, Y. (2022). Continuous Longitudinal Fetus Brain Atlas Construction via Implicit Neural Representation. In: Licandro, R., Melbourne, A., Abaci Turk, E., Macgowan, C., Hutter, J. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2022. Lecture Notes in Computer Science, vol 13575. Springer, Cham. https://doi.org/10.1007/978-3-031-17117-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-17117-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17116-1

  • Online ISBN: 978-3-031-17117-8

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