Abstract
Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. However, existing medical image segmentation networks have issues such as insufficient capability to extract features from target areas, as well as high number of parameters and increased computational complexity. To address these issues, a lightweight Dual-Encoding Feature Interaction Fusion network (DEFIF-Net) is proposed in this paper for medical image segmentation. Firstly, in the encoding stage of DEFIF-Net, a global dependency fusion branch is introduced as an additional encoder to capture distant feature dependencies, whereby the neighboring and distant feature dependencies are effectively integrated by the newly designed feature interaction fusion convolution. Secondly, between the encoder and decoder, channel feature reconstruction modules (CFRMs) are used to enhance the feature representation of important channels. Additionally, a novel multi-branch ghost module (MBGM) is used in the bottleneck layer of the network to enhance its efficiency in capturing and retaining different types of feature information. Lastly, a novel residual feature enhancement (RFE) decoder is utilized to emphasize boundary features, thereby increasing the network's sensitivity to lesion boundaries. The segmentation performance of the proposed DEFIF-Net network is evaluated in two different medical image segmentation tasks. The obtained experimental results demonstrate that, compared to state-of-the-art networks, DEFIF-Net exhibits superior segmentation performance on all three datasets used, while also having a lower parameter count and computational complexity.