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This study introduces UniFLM, a unified framework designed for the automatic segmentation and measurement of fetal limb bones using a newly constructed Fetal Limb Bones (FLB) dataset. By integrating a Semantic-Aware Skip Connection module and a Positive Sampling strategy, UniFLM effectively addresses the challenges of noise and semantic gaps in ultrasound images, leading to improved accuracy in detecting skeletal dysplasias. Experimental results show that UniFLM outperforms existing models, significantly enhancing the assessment of fetal long bone development and congenital anomaly detection.
Achieving superior accuracy in fetal limb assessment, UniFLM bridges critical gaps in ultrasound image analysis that have long hindered the detection of skeletal dysplasias.
Prenatal ultrasound examination is crucial for assessing fetal limb development and detecting congenital anomalies. However, existing artificial intelligence models often overlook fetal lethal skeletal dysplasias due to the lack of high-quality annotated data and a unified framework for multiple long bones. Moreover, generic segmentation models struggle with the inherent noise and semantic gaps in ultrasound images. To address these challenges, we construct the Fetal Limb Bones (FLB) dataset, comprising high-quality annotations for the humerus, femur, tibia-fibula, and radius-ulna. Furthermore, we propose UniFLM, a unified framework for automatic cross-plane segmentation and measurement. UniFLM incorporates a Semantic-Aware Skip Connection module to bridge the semantic gap between encoder and decoder features, and a Positive Sampling strategy to adaptively filter noise and extract essential semantic information. Finally, a Point Regression Mapping module is introduced to learn clinician annotation patterns for precise bone length measurement. Extensive experiments conducted on the FLB dataset demonstrate that the proposed UniFLM achieves superior accuracy and enhanced generalization capabilities in fetal long bone assessment compared to current state-of-the-art models.
