论文标题
使用多级超声扫描分段的婴儿髋关节筛查
Infant hip screening using multi-class ultrasound scan segmentation
论文作者
论文摘要
髋关节（DDH）的发育发育不良是股骨头位于髋关节中不正确的婴儿中的一种疾病。我们提出了一种深度学习算法，用于在超声图像中分割关键结构，并采用此算法来计算股骨头覆盖范围（FHC）并为DDH提供筛查诊断。据我们所知，这是首次自动化DDH筛选的FHC计算的研究。我们的算法优于国际艺术状况，同意我们89.8％的测试图像的专家临床医生的观点。
Developmental dysplasia of the hip (DDH) is a condition in infants where the femoral head is incorrectly located in the hip joint. We propose a deep learning algorithm for segmenting key structures within ultrasound images, employing this to calculate Femoral Head Coverage (FHC) and provide a screening diagnosis for DDH. To our knowledge, this is the first study to automate FHC calculation for DDH screening. Our algorithm outperforms the international state of the art, agreeing with expert clinicians on 89.8% of our test images.
