Patch-based techniques in medical imaging : 4th international workshop, Patch-MI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. Patch-MI (Workshop) (4th : 2018 : Granada, Spain)最新文献

英文中文

Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework. 基于多阶段训练框架的自动上下文深度神经网络的肝组织分类。

Patch-based techniques in medical imaging : 4th international workshop, Patch-MI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. Patch-MI (Workshop) (4th : 2018 : Granada, Spain)

Pub Date : 2018-09-01 Epub Date: 2018-09-15 DOI: 10.1007/978-3-030-00500-9_7

Fan Zhang, Junlin Yang, Nariman Nezami, Fabian Laage-Gaupp, Julius Chapiro, Ming De Lin, James Duncan

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.

在这个项目中，我们的目标是在肝细胞癌患者的三维多参数磁共振图像上对不同类型的肝组织进行分类。在这些情况下，从专家那里获得3D完全注释的分割掩码是昂贵的，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，我们设计了一种新颖的深度卷积神经网络，将自动上下文元素直接集成到类似u -net的架构中。为了在足够数量的样本上进行训练，我们使用了基于补丁的策略和加权抽样程序。此外，我们设计了一个多分辨率和多阶段的训练框架，以减少学习空间并增加模型的正则化。我们的方法在20名患者的图像上进行了测试，并取得了令人鼓舞的结果，优于标准的神经网络方法以及肝组织分类的基准方法。

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