Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods.

Outdoor images taken in haze usually exhibit contrast reduction, color distortion, and detail loss. Removing the haze from a given image is a tough issue owing to its highly ill-posed property. To restore the haze-free image effectively, we develop an unsupervised dehazing method using patch-line and fuzzy clustering-line priors in this paper. The method obtains the dehazed image by inversely solving the atmospheric scattering model, which involves in estimating two key parameters, including atmospheric light and scene transmission. First, the orientation of atmospheric light is achieved by using a patch-line prior. Then, a quadtree subspace hierarchical searching scheme is designed to get the magnitude by calculating the differences between the mean and variance of each component for local regions. Besides, a fuzzy clustering-line prior combined with a guided filtering is proposed to estimate the scene transmission for each pixel. The proposed method can obtain the dehazed image directly without any training process and achieve much better performance than many existing ones with less space and time cost.

Keywords: Image hazing; Semantic segmentation; Feature fusion; Channel attention

Early detection of lung nodules is an important means of reducing the lung cancer mortality rate. In this paper, we propose a three‐dimensional CT image lung nodule detection method based on parallel pooling and dense blocks, which includes two parts, i.e., candidate nodule extraction and false positive suppression. First, a dense U‐shaped backbone network with parallel pooling is proposed to obtain the candidate nodule probability map. The parallel pooling structure uses multiple pooling operations for downsampling to capture spatial information comprehensively and address the problem of information loss resulting from maximum and average pooling in the shallow layers. Then, a parasitic network with parallel pooling, dense blocks, and attention modules is designed to suppress false positive nodules. The parasitic network takes the multiscale feature maps of the backbone network as the input. The experimental results demonstrate that the proposed method significantly improves the accuracy of lung nodule detection, achieving a CPM score of 0.91, which outperforms many existing methods.