Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions’ local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset, an adaptive weighted hybrid loss function is designed for model training. Finally, extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset, with Intersection over Union (IoU), mean Intersection over Union (mIoU), Dice coefficient, and Precision (Pre) of 0.8750, 0.9363, 0.9298, and 0.9012, respectively, which are better than other methods. In addition, its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance.

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.