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。

使用对抗式图像到图像网络进行肝脏自动分割

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Automatic localization and labeling of vertebra in 3D medical images plays an
important role in many clinical tasks, including pathological diagnosis,
surgical planning and postoperative assessment. However, the unusual conditions
of pathological cases, such as the abnormal spine curvature, bright visual
imaging artifacts caused by metal implants, and the limited field of view,
increase the difficulties of accurate localization. In this paper, we propose
an automatic and fast algorithm to localize and label the vertebra centroids in
3D CT volumes. First, we deploy a deep image-to-image network (DI2IN) to
initialize vertebra locations, employing the convolutional encoder-decoder
architecture together with multi-level feature concatenation and deep
supervision. Next, the centroid probability maps from DI2IN are iteratively
evolved with the message passing schemes based on the mutual relation of
vertebra centroids. Finally, the localization results are refined with sparsity
regularization. The proposed method is evaluated on a public dataset of 302
spine CT volumes with various pathologies. Our method outperforms other
state-of-the-art methods in terms of localization accuracy. The run time is
around 3 seconds on average per case. To further boost the performance, we
retrain the DI2IN on additional 1000+ 3D CT volumes from different patients. To
the best of our knowledge, this is the first time more than 1000 3D CT volumes
with expert annotation are adopted in experiments for the anatomic landmark
detection tasks. Our experimental results show that training with such a large
dataset significantly improves the performance and the overall identification
rate, for the first time by our knowledge, reaches 90 %.

本研究提出一种自动定位和标记三维医学图像中椎体中心的算法，使用 DI2IN 进行初始化，使用基于消息传递方案的聚类方法和稀疏性约束进行迭代演进，最终使用稀疏性正则化进行结果优化。该算法在多个病理学数据集上表现出良好的性能。