FeST: Learning Spatio-Temporal Priors for Fetal Brain MRI Segmentation

María F Peñuela, Luisa Vargas, Santiago Usma, María Escobar, Angela Castillo, Pablo Arbeláez

ISBI 2023


Segmentation of anatomic brain structures on fetal magnetic resonance imaging is key in detecting and diagnosing congenital disorders. We propose FeST: a Fetal brain segmentation method, which includes information on the gestational age through Spatio-Temporal priors. We include gestational age in three different priors. We used it as input in our model through a sinusoidal encoding, and in the loss function through KL divergence and a size-prior for modeling the volumetric growth of the brain during development, and that anatomical structures of the brain grow at different rates [1]. We evaluate FeST in the FeTA dataset achieving a Dice similarity coefficient of 0.917, a Volume similarity of 0.974, and a 95th percentile Hausdorff distance of 10.96.

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