MFCPNet: Real time medical image segmentation network via multi-scale feature fusion and channel pruning

Abstract

Real-time medical image segmentation can not only enhance the interactivity and feasibility of applications but also support more medical application scenarios. Local feature extraction methods reliant on Convolutional Neural Networks (CNN) are hampered by restricted receptive fields, which weakens their ability to capture comprehensive information. Conversely, global feature extraction methods based on Transformers generally face impediments in real-time tasks due to their extensive computational demands. To address these challenges and explore accurate and real-time medical image segmentation models, we introduce this novel MFCPNet. MFCPNet begins by devising Multi-Scale Multi-Channel Convolution (MSMC Conv) to extract local features across various levels and scales. This innovative design contributes to extracting richer local information without unduly burdening the model. Second, for the enhanced receptive field of convolution and the model’s generalization capability, we introduce an Attention Block (Attn Block) carrying rotation invariance. This block, inspired by lightweight Bi-Level Routing Attention (BRA) and MLP-Mixer, effectively mitigates the constraints of convolutional structures and achieves superior contextual modeling. Finally, a judicious pruning of the channel count is employed within MFCPNet, striking a trade-off between segmentation accuracy and efficiency. To evaluate the proposed method, we compare it with several classic approaches using three different types of datasets: retinal images, brain scans, and colon polyps. Across these datasets, MFCPNet achieves segmentation performance comparable to existing methods, with a computational cost of 2.2G FLOPs and 0.49M parameters. Furthermore, it demonstrates a processing speed of 79.54 FPS, meeting the requirements for real-time applications.