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Orientation recognition and correction of Cardiac MRI with deep neural network

arXiv 2211.11336 ↗First posted 21 Nov 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

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/

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 21 Nov 2022.

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References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2016Multivariate mixture model for myocardium segmentation combining multi-source imagescited by 133
  4. 2018Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifiercited by 16
  5. 2020Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attentioncited by 15
  6. 2020Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networkscited by 5

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