Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework.
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework.
复制标题
使用基于自动上下文的深度神经网络和多阶段训练框架进行肝脏组织分类。
DOI：
10.1007/978-3-030-00500-9_7
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Duncan,James
中科院分区：
文献类型：
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作者：
Zhang,Fan;Yang,Junlin;Nezami,Nariman;Laage-Gaupp,Fabian;Chapiro,Julius;DeLin,Ming;Duncan,James
在这个项目中，我们的目标是在3D多参数磁共振图像上对肝细胞癌患者的不同类型的肝组织进行分类。在这些情况下，从专家那里获取3D全注释分割掩模是昂贵的，因此可用于训练预测模型的数据集通常很小。为了实现这一目标，我们设计了一种新型的深度卷积神经网络，它直接将自动上下文元素融入到一个类似U-Net的结构中。我们使用了基于补丁的策略和加权采样程序，以便在足够数量的样本上进行训练。此外，我们设计了一个多分辨率、多阶段的训练框架，缩小了学习空间，增加了模型的正则性。我们的方法在20名患者的图像上进行了测试，获得了令人振奋的结果，优于标准的神经网络方法和肝组织分类的基准方法。
In this project, our goal is to classify different types of liver tissue on 3D multi-parameter magnetic resonance images in patients with hepatocellular carcinoma. In these cases, 3D fully annotated segmentation masks from experts are expensive to acquire, thus the dataset available for training a predictive model is usually small. To achieve the goal, we designed a novel deep convolutional neural network that incorporates auto-context elements directly into a U-net-like architecture. We used a patch-based strategy with a weighted sampling procedure in order to train on a sufficient number of samples. Furthermore, we designed a multi-resolution and multi-phase training framework to reduce the learning space and to increase the regularization of the model. Our method was tested on images from 20 patients and yielded promising results, outperforming standard neural network approaches as well as a benchmark method for liver tissue classification.