A preliminary study of a hybrid transformer with enhanced local feature fusion for nuchal translucency ultrasound image segmentation

BACKGROUND: Nuchal translucency (NT) is an important ultrasound indicator in early pregnancy. Increased NT is strongly associated with chromosomal abnormalities, structural defects, and other adverse outcomes. However, the low signal-to-noise ratio (SNR), numerous artifacts, and poor contrast present in ultrasound images pose considerable challenges to existing segmentation methods. PURPOSE: To address these challenges, this paper proposes a novel Transformer model, TransNTSeg, for NT segmentation. This study serves as a preliminary proof-of-concept to evaluate the feasibility of the proposed architecture. METHODS: effect size. RESULTS: TransNTSeg achieves the highest performance on the test dataset, with a mean IoU of 0.763 ± 0.05 and a mean DSC of 0.852 ± 0.03. Rigorous statistical analysis (Bonferroni corrected p < 0.0015625) confirms considerable improvements. Based on the evaluation of effect sizes, TransNTSeg is identified as the best-performing method, demonstrating a substantial effect size (d = 0.745) against the closest competitor (MT-UNet). CONCLUSION: While demonstrating competitive performance within this controlled setting, these promising outcomes highlight the potential of TransNTSeg for accurate NT segmentation. This warrants further validation on multicenter, multi-vendor datasets to establish its generalizability for broad clinical use.