Automatic segmentation of placenta from MR images using a novel BiGC U-Net

Accurate segmentation of the placenta in Magnetic Resonance (MR) images is required for quantitative techniques such as texture and shape analysis, which have been proposed to improve placenta accreta spectrum (PAS) diagnostic rates. However, it is challenging due to the low contrast, image noise, blurred boundaries, and manual annotation variability. Hence, we proposed an enhanced U-Net architecture named BiGC U-Net, to automatically segment placental MR images. This deep learning (DL) structure incorporates a bidirectional gated convolutional module (BiGC) to capture complementary spatial dependencies, a hierarchical regularization mechanism (HRM) to enhance crosslayer semantic consistency, and an innovative data augmentation strategy to synthesize new images. The performance of BiGC U-Net was evaluated on three placental MR datasets: (1) public, (2) Sheffield Teaching Hospitals (STH) and (3) combined (public + STH + augmented), and compared against existing DL models including U-Net, Attention U-Net, ResNet, UNet++, TransUNet, nnUNet, and SSM-Mamba. The BiGC U-Net exhibited the best Dice similarity coefficient (90.74 ± 0.44), 95th percentile Hausdorff distance (3.84 mm ± 0.53 mm) and relative volume difference (9.06 ± 0.41) in comparison with other DL models in the combined dataset. These findings indicate the effectiveness and robustness of the BiGC U-Net in accurately and automatically segmenting the placenta.