UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image
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
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “UniFLM: United Segmentation and Measurement on Fetal Limb Ul” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Zeen Zhou →
“UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image”
- LinkedLinked via arxiv author · 85%Qiuhua Chen →
“UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image”
- LinkedLinked via arxiv author · 85%Xiaojun Cao →
“UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image”
- LinkedLinked via arxiv author · 85%Changmao Chen →
“UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image”