MM-UKAN++: A Novel Kolmogorov-Arnold Network Based U-shaped Network for Ultrasound Image Segmentation.

Abstract

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROI) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROI. Existing convolutional neural network (CNN)-based and Transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold Networks (KANs). MM-UKAN++ leverages multi-level KAN layers as the encoder and decoder within the U-network architecture, and incorporates an innovative multi-dimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. Additionally, the network effectively integrates multi-scale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31mm HD in the BUSI dataset with 3.17G Flops and 9.90M Parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub.