Dilated dendritic learning of global–local feature representation for medical image segmentation

Medical image segmentation serves as an important tool in the treatment of various medical diseases. However, achieving precise and efficient segmentation remains challenging due to the intricate structures and variations. Although neural network methods based on U-shaped structures have shown impressive results, they often lack effective representation of global–local features, leading to insufficient extraction of multi-scale and contextual information in medical image segmentation tasks. To tackle these challenges, we propose a novel approach: a dilated dendritic module with deep supervision, namely 3DL-Net. It integrates the flexible dilated convolution mechanism into the segmentation architecture, aiming to expand the model's receptive field and capture richer global features. Additionally, in contrast to other segmentation architectures, we innovatively introduce the processing of local feature of shallow structures through the dendritic neuron module in medical images into 3DL-Net. This is the first time dendritic learning has been employed at the channel level and represents a pioneering approach to the local feature process. During the training process, 3DL-Net incorporates a deep supervision mechanism that utilizes our designed loss function. Due to the intermediate supervision signals at various network stages, providing feedback at multiple levels, the model refines its predictions across various scales, contributing to further enhanced segmentation outcomes. To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three medical image datasets to demonstrate significant improvements in segmentation accuracy compared to state-of-the-art models. Our mDice metrics on three datasets achieved 86.61%, 87.87%, and 85.06%, surpassing the second-best models by 3.85%, 1.54%, and 0.95%.