Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. In this paper, we propose an automatic and efficient
algorithm to segment liver from 3D CT volumes. A deep image-to-image network
(DI2IN) is first deployed to generate the liver segmentation, employing a
convolutional encoder-decoder architecture combined with multi-level feature
concatenation and deep supervision. Then an adversarial network is utilized
during training process to discriminate the output of DI2IN from ground truth,
which further boosts the performance of DI2IN. The proposed method is trained
on an annotated dataset of 1000 CT volumes with various different scanning
protocols (e.g., contrast and non-contrast, various resolution and position)
and large variations in populations (e.g., ages and pathology). Our approach
outperforms the state-of-the-art solutions in terms of segmentation accuracy
and computing efficiency.

本文提出了一种自动高效的算法（DI2IN），利用卷积编码器 - 解码器结构和深度监督联合多级特征级联，采用对抗网络进行训练来辅助区分 DI2IN 的输出和真实数据，最终证明本方法在各种扫描协议和人群差异情况下能够取得比现有解决方案更高的肝脏分割精度和计算效率。