Contour-induced parallel graph reasoning for liver tumor segmentation.

[Display omitted] • Employ contour features as geometric priors to preserve the outline details of lesions and explicitly model the correlation between region and contour. • Propose a parallel graph reasoning strategy to model the long-range relationship between adjacent pixels and analyze the global dependencies along the channel dimension of feature maps. • The improvements of the proposed method compare to the state-of-the-art models are validated on two public LiTS17 and 3DIRCADb datasets. The accurate detection and segmentation of liver cancers from abdominal CT scans is critical. However, segmenting liver tumors presents significant hurdles due to indistinct lesion boundaries and ignoring the correlation between target and outlines. In this paper, we propose the Parallel Graph Convolutional Network (PGC-Net), a completely novel segmentation framework for liver tumors. With regard to segmentation against constraints, we specifically use contour-induced parallel graph reasoning for quick yet efficient segmentation. First, we use a Pyramid Vision Transformer that has already been trained to extract multi-scale features of region and contour. In order to project the pixels into two distinct high-dimensional areas, we secondly use the parallel graph reasoning strategy, where the vertices are weighted in accordance with the geometric prior of the contour. Through the process of graph convolution, the complementary properties of region and contour also propagate the information. Finally, we project back to the original pixel space for the prediction using the refined features deduced from the graph. Experimental results on two available datasets, LiTS17 (with an average Dice score of 73.63%) and 3DIRCADb (with an average Dice score of 74.16%). Our framework focused on the interaction between two orthogonal graphs and contour information, which has the potential to improve the accuracy and efficiency of liver tumor segmentation. [ABSTRACT FROM AUTHOR]