Dendritic Kernel Convolutional Neural Network for Breast Ultrasound Images Segmentation

Breast tumor segmentation in ultrasound images remains a challenging task due to low contrast, acoustic shadowing, and heterogeneous tumor appearance. Traditional deep learning-based segmentation models often perform poorly in addressing these challenges, making it difficult to accurately capture fine-grained tumor boundaries and complex structural variations. To address these issues, we propose a novel component—dendritic kernel convolution, inspired by the synaptic integration and inhibition mechanisms of biological neurons. Unlike traditional convolutional kernels that perform only linear weighting operations, dendritic kernel convolution simulates the nonlinear excitation and inhibition mechanisms of dendritic computation, adjusting feature aggregation and boundary optimization strategies. This mechanism enhances key information while suppressing noise, effectively reducing missed detections and erroneous segmentations, thereby improving segmentation accuracy and robustness. Inspired by the hierarchical processing mechanism of the human visual system—which progresses from coarse to fine perception—we further design a multistage refinement architecture. Based on dendritic kernel convolution, we construct two key modules: a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dendritic-dilated convolution module</b> and a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dendritic U-Net module</b>, and integrate them into a unified framework, termed the dendritic kernel convolutional neural network (DKNet) for breast tumor segmentation. To assess the segmentation performance of the proposed network, we conduct a comparative analysis against several state-of-the-art segmentation methods using seven quantitative metrics. The experimental results unequivocally demonstrate that DKNet surpasses all other methods, exhibiting superior segmentation outcomes and affirming its efficacy for breast tumor segmentation.