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Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image segmentation. Specifically, we first design a simplified residual module to prevent network degradation. Given the scale variability of objects, we propose a multi-scale context extraction module by combining four cascaded branches of hybrid dilated convolutions to capture broader and deeper features. In addition, we introduce an external attention mechanism based on two external, learnable and shared memory units, which helps to perceive the most discriminative information and suppress redundant features. Finally, we provide a boundary correction block to further improve the localization ability of boundary information. Extensive experiments on two liver CT image benchmark datasets qualitatively and quantitatively illustrate that our method is effective in improving liver segmentation accuracy and outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the gray values of liver and surrounding tissues and organs are resemblance in abdominal computed tomography(CT) images, it is difficult to accurately determine the boundary of liver images. To address the aforementioned issues, we propose a segmentation method based on dynamic adaptive residual network (DAR-net). Firstly, inspired by the U-Net network, a dynamic adaptive pooling strategy based on interpolation optimization is proposed to process all features in the pooling domain. Subsequently, the skip connections and a residual structure are introduced into the network to improve the generalization ability and convergence speed of DAR-net. Finally, removing irrelevant pixels and condition random fields are used to optimize the boundaries and textures of liver to avoid the inaccuracy of liver boundaries. Experiments on a standard 3DIRCADB dataset demonstrate that the DAR-net can obtain the average Dice score is 96.13%, which increases by 13.02% compared to the prediction result without any processing. [ABSTRACT FROM AUTHOR]