BRM-UNet: A Novel Framework for High-Precision Fetal Heart Segmentation in Ultrasound Images

Segmenting fetal heart ultrasound images presents significant challenges due to variations in image quality, noise, and the small size of the target region. This paper introduces the BRM-UNet framework, which incorporates multiscale feature learning, boundary refinement, and a selective scanning mechanism to overcome the limitations of current methods. The proposed model utilizes a weighted loss function that combines Dice and boundary-aware losses, enhancing both segmentation accuracy and boundary precision. Moreover, the selective scanning mechanism improves feature extraction by prioritizing the heart region, ensuring more accurate segmentation. Experimental results on the FOCUS dataset show that our model outperforms existing approaches, achieving superior segmentation performance (Dice coefficient 0.9178, IoU 0.8500) and boundary precision (Hausdorff distance 27.2503). Further validation on a pediatric cardiac ultrasound dataset yielded excellent results (Left atrium Dice 0.9000, Left ventricle Dice 0.9035), demonstrating the model's robust generalization and its potential as a more accurate and automated solution for both fetal and pediatric cardiac ultrasound assessments.