Automatic segmentation of the liver and hepatic lesions is an important step
towards deriving quantitative biomarkers for accurate clinical diagnosis and
computer-aided decision support systems. This paper presents a method to
automatically segment liver and lesions in CT and MRI abdomen images using
cascaded fully convolutional neural networks (CFCNs) enabling the segmentation
of a large-scale medical trial or quantitative image analysis. We train and
cascade two FCNs for a combined segmentation of the liver and its lesions. In
the first step, we train a FCN to segment the liver as ROI input for a second
FCN. The second FCN solely segments lesions within the predicted liver ROIs of
step 1. CFCN models were trained on an abdominal CT dataset comprising 100
hepatic tumor volumes. Validations on further datasets show that CFCN-based
semantic liver and lesion segmentation achieves Dice scores over 94% for liver
with computation times below 100s per volume. We further experimentally
demonstrate the robustness of the proposed method on an 38 MRI liver tumor
volumes and the public 3DIRCAD dataset.

该论文利用级联全卷积神经网络自动进行肝脏和肝脏病变的分段，以实现高质量的医学影像分析和临床诊断。论文结果表明，其分割结果和计算时间均较为准确和快速。

使用级联完全卷积神经网络自动分割 CT 和 MRI 体积中的肝脏和肿瘤

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using  Cascaded Fully Convolutional Neural Networks

In this paper we present a methodology of classifying hepatic (liver) lesions
using multidimensional persistent homology, the matching metric (also called
the bottleneck distance), and a support vector machine. We present our
classification results on a dataset of 132 lesions that have been outlined and
annotated by radiologists. We find that topological features are useful in the
classification of hepatic lesions. We also find that two-dimensional persistent
homology outperforms one-dimensional persistent homology in this application.

本文提出了一种用多维持续同调、匹配度量和支持向量机分类肝脏病变的方法，并在 132 个已经被放射科医生勾勒和注释的病变数据集上进行了分类结果的展示。我们发现拓扑特征在肝脏病变分类中是有用的，二维持续同调在此应用中优于一维持续同调。