论文标题
通过多任务学习和深层神经网络识别和标准化心脏MRI取向
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks
论文作者
论文摘要
在本文中，我们研究心脏MRI成像取向的问题，并提出了一个框架，以通过深层神经网络对识别和标准化的方向进行分类。该方法使用新的多任务策略，同时实现了心脏分割的任务和方向识别的任务。对于MRI的多种序列和模态，我们提出了一种转移学习策略，该策略将我们提出的模型从单个模态调整到多种方式。我们将方向识别网络嵌入心脏MRI取向调整工具中，即Cmradjustnet。我们实施了两个版本的CMRADJUSTNET，包括用户界面（UI）软件和命令行工具。前版本支持MRI图像可视化，方向预测，调整和存储操作；后一个版本可以实现批处理操作。源代码，神经网络模型和工具已通过https://zmiclab.github.io/projects.html发布并打开。
In this paper, we study the problem of imaging orientation in cardiac MRI, and propose a framework to categorize the orientation for recognition and standardization via deep neural networks. The method uses a new multi-tasking strategy, where both the tasks of cardiac segmentation and orientation recognition are simultaneously achieved. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We embed the orientation recognition network in a Cardiac MRI Orientation Adjust Tool, i.e., CMRadjustNet. We implemented two versions of CMRadjustNet, including a user-interface (UI) software, and a command-line tool. The former version supports MRI image visualization, orientation prediction, adjustment, and storage operations; and the latter version enables the batch operations. The source code, neural network models and tools have been released and open via https://zmiclab.github.io/projects.html.
