关键词: 息肉分割, 域自适应, 多尺度特征聚合, 医学图像处理

Abstract: The deep learning method has achieved remarkable success in polyp image segmentation. However, there are still two issues hindering the advancement of high-precision polyp recognition. Firstly, there exists a disparity in the distribution of image features collected from different devices, posing a challenge of domain shift across various datasets. Secondly, the prevailing models predominantly focus on handling features of the same scale, limiting the capacity to capture multi-scale features. To address these issues, a domain adaptation module and a multi-scale feature aggregation module are proposed. The domain adaptation module achieves adaptation to the variances among images derived from distinct domains via an unsupervised approach. Then, the domain-adapted images are processed through an encoder to obtain feature maps with different receptive fields. By employing the proposed multi-scale feature aggregation module, the feature maps with difference receptive fields are fused, enhancing the  ability of model to segment lesions of different scales. On five public polyp segmentation datasets, the proposed method is compared with widely used colonic polyp segmentation methods. The proposed method achieves better results on the Dice and IoU metrics compared to the contrastive methods, particularly on the Kvasir and ClinicDB datasets. Furthermore, the model exhibits consistent segmentation performance employed for assessing generalization ability. In conclusion, the fusion of the domain-adaptive network and the multi-scale feature aggregation in the polyp segmentation network effectively facilitates the segmentation of polyp images while demonstrating commendable generalization performance.

Key words: polyp segmentation, domain adaptation, multi-scale feature aggregation, medical image processing