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Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework

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Information Processing in Medical Imaging (IPMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9123))

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Abstract

Understanding the early dynamics of the highly folded human cerebral cortex is still an actively evolving research field teeming with unanswered questions. Longitudinal neuroimaging analysis and modeling have become the new trend to advance research in this field. However, this is challenged by a limited number of acquisition timepoints and the absence of inter-subject matching between timepoints. In this paper, we propose a novel framework that unprecedentedly solves the problem of predicting the dynamic evolution of infant cortical surface shape solely from a single baseline shape based on a spatiotemporal (4D) current-based learning approach. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we first use the current-based shape regression model to set up the inter-subject cortical surface correspondences at baseline of all training subjects. We then estimate for each training subject the diffeomorphic temporal evolution trajectories of the cortical surface shape and build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we first warp all training subjects onto its baseline cortical surface. Second, we select the most appropriate learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints from its baseline cortical surface, based on closeness metrics between this baseline surface and the learnt baseline population average surface atlas. We used the proposed framework to predict the inner cortical surface shape at 3, 6 and 9 months from the cortical shape at birth in 9 healthy infants. Our method predicted with good accuracy the spatiotemporal dynamic change of the highly folded cortex.

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Acknowledgments

We kindly thank Deformetrica research team [11] for making their source code available at www.deformetrica.org.

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Correspondence to Dinggang Shen .

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Rekik, I., Li, G., Lin, W., Shen, D. (2015). Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-19992-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19991-7

  • Online ISBN: 978-3-319-19992-4

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