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 t-test. The observed/expected ratio of the total lung volume (O/E TLV) as a prognostic marker for postnatal survival was calculated. Outcome predictions of O/E TLV for postnatal death were assessed with univariate regression analysis.

Results: Manual segmentation times were higher compared to DL segmentations (30 ± 7 minutes versus 0.25 ± 0.05 minutes, P < .001). Manual and DL-based TFBV were similar (1317 ± 498 mL versus 1306 ± 491 mL; P = .04; Dice score: 0.98 ± 0.01). TFLV (19.4 ± 11.5 mL versus 18.7 ± 12.4 mL; P = .11; Dice score: 0.84 ± 0.09) and O/E TLV (39.3 ± 18.1 mL versus 37.7 ± 19.1 mL, P = .13) were not significantly different. Postnatal mortality was negatively associated with higher manual O/E TLV (odds ratio: 0.97; 95% confidence interval [CI], 0.96-0.98; P < .001) and DL O/E TLV (odds ratio: 0.97; 95% CI, 0.96-0.98; P < .001).

Conclusion: DL for body and lung segmentation in fetuses with congenital diaphragmatic hernia allows reliable and rapid calculations of the observed/expected ratio and equally predicts prognostic outcome.