Objective: Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that focus on the performance of generalization and accuracy. Methods: To improve the generalization performance, we initially propose an auto-context algorithm in a single CNN. The proposed auto-context neural network exploits an effective high-level residual estimation to obtain the shape prior. Identical dual paths are effectively trained to represent mutual complementary features for an accurate posterior analysis of a liver. Further, we extend our network by employing a self-supervised contour scheme. We trained sparse contour features by penalizing the ground-truth contour to focus more contour attentions on the failures. Results: We used 180 abdominal CT images for training and validation. Two-fold cross-validation is presented for a comparison with the state-of-the-art neural networks. The experimental results show that the proposed network results in better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance. Novel multiple $N$-fold cross-validations are conducted to show the best performance of generalization of the proposed network. Conclusion and significance: The proposed method minimized the error between training and test images more than any other modern neural networks. Moreover, the contour scheme was successfully employed in the network by introducing a self-supervising metric.

目标：肝脏的准确图像分割是一个具有挑战性的问题，因为它的形状可变性大且边界不明确。尽管全卷积神经网络 (CNN) 的应用已经显示出开创性的成果，但有限的研究集中在泛化的性能上。在这项研究中，我们引入了一个 CNN，用于对腹部计算机断层扫描 (CT) 图像进行肝脏分割，专注于泛化和准确性的性能。方法：为了提高泛化性能，我们最初在单个 CNN 中提出了一种自动上下文算法。所提出的自动上下文神经网络利用有效的高级残差估计来获得先验形状。有效地训练相同的双路径以表示用于肝脏的准确后验分析的互为补充的特征。此外，我们通过采用自我监督的轮廓方案来扩展我们的网络。我们通过惩罚真实轮廓来训练稀疏轮廓特征，以将更多的轮廓注意力集中在失败上。结果：我们使用了 180 张腹部 CT 图像进行训练和验证。提出了双重交叉验证以与最先进的神经网络进行比较。实验结果表明，与最先进的网络相比，所提出的网络通过减少 10.31% 的 Hausdorff 距离获得了更好的准确性。小说多$N$进行 -fold 交叉验证以显示所提出网络的最佳泛化性能。结论和意义：与任何其他现代神经网络相比，所提出的方法最大限度地减少了训练和测试图像之间的误差。此外，通过引入自我监督度量，在网络中成功采用了轮廓方案。