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      Cardiac Segmentation Workflow

      This tool uses a fully convolutional neural network to automatically segment the left ventricle, the left ventricle myocardium and the right ventricle of cine-MRI cardiac images from three possible views (short axis, four-chamber long axis and two-chamber long axis). It is based on the Github repository DLTK models: UKBB cardiac segmentation cine-MRI.


      The tool detects the end-systolic and end-diastolic frames in the image and uses the information to compute a set of clinical values:

      • Left ventricular end-diastolic volume (LVEDV)
      • Left ventricular end-systolic volume (LVESV)
      • Left ventricular myocardial mass (LVM)
      • Right ventricular end-diastolic volume (RVEDV)
      • Right ventricular end-systolic volume (RVESV)

      The full workflow includes: - DICOM files to NIfTI conversion and reorientation to standard. - Cardiac segmentation using fully convolutional neural network.

      Required inputs

      • One or more cine-MRI cardiac images.
      • The inputs must be labeled as either:
        • cardiac short_axis 4d
        • cardiac long_axis 2ch 4d
        • cardiac long_axis 4ch 4d

      Outputs

      For each of the input images the following files are outputted:

      • Original image in 4D
      • Segmentation of the original image
      • End-diastolic frame of the original image
      • Segmentation of the end-diastolic frame
      • End-systolic frame of the original image
      • Segmentation of the end-systolic frame
      • Clinical measures in a csv file
      • Report for quality control purposes

      Advanced settings

      • DCM2NII:
        • Preferred DICOM to NIfTI conversion tool (drop-down selection):
          • DCM2niix (Default)
          • Mrtrix
          • DCM2nii
          • diffunpack
          • MRIConvert

      References

      • [1] W. Bai, et al. Human-level CMR image analysis with deep fully convolutional networks. https://doi.org/10.48550/arXiv.1710.09289

      • [2] S. Petersen, et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort. Journal of Cardiovascular Magnetic Resonance, 19:18, 2017. https://doi.org/10.1186/s12968-017-0327-9

       


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