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Table 1 Demographics and MRI sequence information

From: ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates

  Training DHCP* (N = 473) ECHO-Dataset1ƒ (N = 20) M-CRIB (N = 10) ECHO-Dataset2 (N = 50)
PMA at scan, weeks 40.65 ± 2.19 46.90 ± 4.14 39.78 ± 1.31 48.06 ± 4.69
Sex     
 Female, N(%) 266 (43.8%) 13 (65.0%) 4 (40.0%) 25 (50%)
 Male, N (%) 207 (56.2%) 7 (35.0%) 6 (60.0%) 25 (50%)
MRI scanners 3  Philips 3T GE 3T Siemens 3T GE
MRI resolution (mm3) [0.5, 0.5, 0.5] [0.9, 0.9, 0.9] [0.63, 0.63, 0.63] [0.9, 0.9, 0.9]
MRI dimensions [290,290,203] [130,256,256] [304,304,157] [130, 256, 256]
  1. For quantitative variables, data are presented as mean ± standard deviation unless otherwise noted. PMA: postmenstrual age. *The large training DHCP dataset with corresponding dHCP labels was used to pre-train the model with sufficient data. ƒThe ECHO-Dataset1 dataset was used to test the model’s performance as an internal source. The M-CRIB dataset was used as an external test dataset to further test the reliability of our proposed deep learning framework. The proof-of-concept ECHO-Dataset2 was used to test the association between brain morphometric measures at birth and corresponding CBCL measures at age 2-year-old.