Development of Gestational Age–Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning

Method for training and testing the U-Nets. A, Schematic of 3D U-Net architecture used for training with sample input and output patch is shown. B, Manually segmented images were split into training (n = 36 patients) and test (n = 10 patients) sets. C, After confirmation of adequate segmentation performance on the test set, the trained U-Nets were applied to an additional 200 fetal brain MRIs for calculating normal fetal intracranial and brain volumes across GAs.

Performance of the U-Net for automated segmentation of intracranial (blue) and brain (green) volumes on the test set. A, Representative examples of the segmentation overlay on a section of the original brain MR imaging in various acquisition planes across multiple gestational ages (w = weeks, d = days). B, Individual Dice scores and boxplots compare the automated with the ground truth manual segmentation within axial, coronal, and sagittal dimensions, distinguishing scores for intracranial and brain segmentations. C, Bland-Altman plot demonstrates no linear trend in the difference between manual and automatically calculated intracranial volumes across the range of volumes tested. Each type of marker corresponds to axial, sagittal, and coronal measurements on an individual fetal brain. D, Scatterplot demonstrates strong agreement between manual and automated intracranial segmentation volumes, color-coded by GA. The best fit line and the 1:1 identity line are shown, nearly overlapping.

Intracranial (blue) and brain (green) volumes as a function of GA across 184 fetal MRIs with normal findings. Individual points represent automated measurements of volume in individual fetal brain MRIs. The center line represents a moving average across these points, ±1 (dark shading) or 2 (light shading) SDs. One fetal brain is shown as an example in the insets. Images shown in Figure 3A are denoted by a +.