FEST: LEARNING SPATIO-TEMPORAL PRIORS FOR FETAL BRAIN MRI SEGMENTATION

被引:0
|
作者
Penuela, Maria F. [1 ]
Vargas, Luisa [1 ]
Usma, Santiago [1 ]
Escobar, Maria [1 ]
Castillo, Angela [1 ]
Arbelaez, Pablo [1 ]
机构
[1] Univ Los Andes, Ctr Res & Format Artificial Intelligence, Bogota, Colombia
关键词
Fetal brain segmentation; Deep learning; Fetal-MRI; Gestational age; U-Net;
D O I
10.1109/ISBI53787.2023.10230531
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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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页数:5
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