Problem
Research questions and friction points this paper is trying to address.
fetal limb development
skeletal dysplasias
ultrasound images
segmentation models
UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image
🤖 AI Summary
为解决胎儿肢体超声图像中骨骼发育评估及先天性异常检测问题，提出UniFLM框架，通过高质量数据集和创新模块提高分割与测量精度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified Framework
Semantic-Aware Skip Connection
Positive Sampling
Point Regression Mapping
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