Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy
This work addresses a domain-specific issue for medical imaging practitioners by providing an incremental improvement in efficiency for cardiac MRI processing.
The paper tackled the problem of automatically recognizing orientation in cardiac MRI to avoid manual reorientation, using deep neural networks and a transfer learning strategy for multiple sequences and modalities, with results showing improved accuracy through a voting prediction method.
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.