In most medical image processing tasks, the orientation of an image would affect computing result. However, manually reorienting images wastes time and effort. In this paper, we study the problem of recognizing orientation in cardiac MRI and using deep neural network to solve this problem. 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 also propose a prediction method that uses voting. The results shows that deep neural network is an effective way in recognition of cardiac MRI orientation and the voting prediction method could improve accuracy.

翻译：在大多数医学图像处理任务中,图像的方向会影响计算结果。然而,手工调整图像的方向会浪费时间和精力。在本文中,我们研究了在心脏MRI中识别方向和使用深神经网络解决这一问题的问题。对于磁共振的多个序列和模式,我们提出了一个转移学习战略,将我们提议的模型从单一模式调整为多种模式。我们还提出了一个使用投票的预测方法。结果显示,深神经网络是确认心脏MRI方向的有效方法,投票预测方法可以提高准确性。