A novel medical image segmentation network for colorectal polyp small targets and fuzzy boundaries
TL;DR
Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization, and significantly improving clinical diagnosis efficiency and accuracy.
Abstract
Abstract Objectives To address the challenges of complex feature variations and unclear boundary definitions between segmented targets and surrounding regions in medical images, a novel segmentation model based on Deformable Large Kernel Convolutional Attention (D-LKA) and Transformer is proposed. Methods The model first uses Vision Transformer as the encoder to enhance the ability to capture global information, overcoming the limitations of convolutional neural networks’ receptive field. In the decoder, a D-LKA decoder with deformable large kernel convolution attention is used, allowing the model to adapt to complex target features. Finally, the TRR module is introduced to coordinate information transfer between the convolutional neural network and Transformer, reducing semantic loss. Results The model is trained, validated, and tested on the Kvasir-SEG colon polyp dataset, with multiple ablation experiments. To validate generalization, experiments are also conducted on the CVC-ClinicDB dataset. Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization. Conclusions TDU-Net achieves excellent segmentation results, addressing small target and incomplete feature extraction issues, significantly improving clinical diagnosis efficiency and accuracy.