12.25. Registration Pipeline

Registration pipeline takes a data folder and serveral registration parameters as input. Input data can be either in DICOM or nifti format. Pipeline uses image-registration operator for registering input datsets to an input target data.

  • NV_REG_TARGET: Registration target name. (Folder name if the input is in Dicom format, image name if input is in nifti format)

  • NV_REG_SEQUENCE: Sequence of registration operations, separated by “::”.

  • NV_OUT_SEQUENCE: Sequence of registration data in case of multi channel output, separated by “::”

  • NV_REG_INPUT: Input file format. Supported types: dicom, nifti

  • NV_REG_OUTPUT: Output file format. Supported types: nifti_series, nifti

  • NV_REG_TRANSFORM: Registration Transformation, supported types: affine

  • NV_REG_METRIC: Registration Metric. Supported types: mattes_mutual

  • NV_REG_MM_BINS: Mattes Mutual information metric bin size, typical range 10-60

  • NV_REG_MM_SAMPLING_PERCENTAGE: Mattes Mutual Sampling percentage, typical value .01 to .5

  • NV_REG_OPTIMIZER: Optimizer, Supported types: reg_step_grad_desc

  • NV_REG_RSGD_ITERATIONS: Iterations of regular step gradient descent optimzer

  • NV_REG_RSGD_MAXSTEP: Regular step gradient descent optimzer learning rate, typical range 1.0 to 5.0

  • NV_REG_RSGD_MINSTEP: Regular step gradient descent optimzer minimum step 0.001

  • NV_REG_RSGD_RELAXATION: Regular step gradient descent optimzer relaxation factor

  • NV_REG_INTERPOLATOR: Interpolator in Registration. Supported types: linear

The following are the execution steps for registration pipeline:

  • Platform Installation: Ensure that platform is installed successfully and Clara CLI is installed correctly.

  • Required Operator: Following operator must be installed. If the operator is not present in the system, either build it locally or pull it from https://ngc.nvidia.com/containers.

    • image-registration

    Update the pipeline definition with appropriate container image and tag information. Ensure that container image and tag are present in the system before executing the pipeline.

  • Execute following in sequence using Clara cli:


    clara create pipeline -p <path to registration pipeline> clara create jobs -n <name> -p <pipeline ID> -f <path to input data folder> clara start job -j <JOB ID>

  • Once job is complete, check payloads for the registered output

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