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One primary challenge in liver segmentation is the fuzzy edge contour. Recently, fully convolutional neural networks (FCNs) have been widely used in liver segmentation because of their adequate feature extraction. Nevertheless, the context among liver slices is still ignored by FCN. To address this issue, we first propose a bidirectional convolutional long short-term memory (BiConvLSTM) to explore contextual information. Meanwhile, the attention gate (AG) is utilized to fuse high-dimensional information from BiConvLSTM to remove irrelevant features. Besides, Shape-Net network is proposed to extend the liver shape pattern by latent space information, which will contribute to reduce the interference of fuzzy boundaries. Finally, the improved active contour loss function with L2 norm acts as a feature constraint. Experimental results on public benchmark datasets show that the proposed method slightly outperforms other newly published methods and achieves good performance for liver segmentation. [ABSTRACT FROM AUTHOR]