用小波神经算子提升胎儿头部分割精度，兼顾时频定位与空间模式捕捉。
U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation
- 融合小波分解与编解码结构，增强对超声图像的多尺度特征提取能力。
- 在不同孕周二维超声数据上实现高精度胎儿头部分割，提升临床应用可行性。
- 适合医学影像分析、产科辅助诊断领域研究人员快速了解新型深度学习方法。
本文提出一种新型U-Net增强型小波神经算子（U-WNO），结合小波分解、算子学习与编码器-解码器结构。该方法利用小波在函数时频局部化方面的优势，通过下采样与上采样操作生成分割图，实现对空间域模式的精准追踪和功能映射的有效学习，完成区域分割。U-WNO在不同妊娠阶段的二维超声图像上进行了验证，展示了其在理论进展与实际应用之间的桥梁作用，有望在多个科学与工业领域带来显著影响，推动更精准的决策制定与运营效率提升。
原文摘要 · Abstract (English)
This article describes the development of a novel U-Net-enhanced Wavelet Neural Operator (U-WNO),which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism. This approach harnesses the superiority of the wavelets in time frequency localization of the functions, and the combine down-sampling and up-sampling operations to generate the segmentation map to enable accurate tracking of patterns in spatial domain and effective learning of the functional mappings to perform regional segmentation. By bridging the gap between theoretical advancements and practical applications, the U-WNO holds potential for significant impact in multiple science and industrial fields, facilitating more accurate decision-making and improved operational efficiencies. The operator is demonstrated for different pregnancy trimesters, utilizing two-dimensional ultrasound images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。