Fetal MRI deep learning segmentation of body and lung in congenital diaphragmatic hernia.

ABSTRACT:

Purpose

To determine if deep learning (DL) segmentation of total fetal body volume (TFBV) and total fetal lung volume (TFLV) in fetuses with congenital diaphragmatic hernia has comparable performance to manual segmentation.

Materials and methods

A total of 208 fetal MRI studies with congenital diaphragmatic hernia, acquired between August 2007 and September 2023, were retrospectively included. TFBV and TFLV were extracted from manual tissue segmentations in balanced gradient echo and single shot T2-weighted turbo spin echo sequences. MRI studies were split into training (n = 188) and hold-out test data (n = 20). Wilcoxon signed-rank test was used to compare manual and DL-based segmentations by 2 U-Nets. Manual and DL segmentation times were noted and compared using Student's

SUBMITTER: Bischoff LM 

PROVIDER: S-EPMC12429181 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature