U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation

TL;DR

A novel U-Net-enhanced Wavelet Neural Operator (U-WNO) is developed, which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism to enable accurate tracking of patterns in spatial domain and effective learning of the functional mappings to perform regional segmentation.

Abstract

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.

Figures (3)

• Figure 1: 2D USG Image (left) and it's mask(right). This is a sample from the used dataset.
• Figure 2: U-Net-enhanced Wavelet Neural Operator(U-WNO) flow
• Figure 3: Samples from the test set and their corresponding segmentation outputs obtained through U-WNO. The top and bottom rows show two different test samples and their respective results.