Orientation recognition and correction of Cardiac MRI with deep neural network
This addresses a domain-specific problem for medical imaging practitioners, but it is incremental as it applies existing deep learning methods to a new task.
The paper tackles orientation correction in cardiac MRI images by proposing a deep neural network framework for orientation recognition, which is embedded into a command-line tool for 2D DICOM and 3D NIFTI images.
In this paper, the problem of orientation correction in cardiac MRI images is investigated and a framework for orientation recognition via deep neural networks is proposed. For multi-modality MRI, we introduce a transfer learning strategy to transfer our proposed model from single modality to multi-modality. We embed the proposed network into the orientation correction command-line tool, which can implement orientation correction on 2D DICOM and 3D NIFTI images. Our source code, network models and tools are available at https://github.com/Jy-stdio/MSCMR_orient/