Running RegSeg on Docker

1. Install docker

https://www.docker.com/get-docker

2. Run the help command

docker run -it oesteban/regseg --help

This should give you the following output:

Usage:

General options:
  -h [ --help ]                         show help message
  -F [ --fixed-images ] arg             fixed image file
  -P [ --surface-priors ] arg           shape priors
  -T [ --surface-target ] arg           final shapes to evaluate metric (only 
                                        testing purposes)
  -M [ --fixed-mask ] arg               fixed image mask
  -L [ --transform-levels ] arg         number of multi-resolution levels for 
                                        the transform
  -o [ --output-prefix ] arg (=regseg)  prefix for output files
  -l [ --logfile ] arg                  log filename
  -v [ --monitoring-verbosity ] arg (=1)
                                        verbosity level of intermediate results
                                        monitoring ( 0 = no output; 5 = verbose
                                        )

Optimizer options (by levels):
  -a [ --alpha ] arg              alpha value in regularization
  -b [ --beta ] arg               beta value in regularization
  -s [ --step-size ] arg          step-size value in optimization
  -g [ --gradient-scales ] arg    alpha value in regularization
  -r [ --learning-rate ] arg      learning rate to update step size
  -i [ --iterations ] arg         number of iterations
  -w [ --convergence-window ] arg number of iterations of convergence window
  -t [ --convergence-thresh ] arg convergence value
  --grid-size arg                 size of control points grid
  --grid-spacing arg              spacing between control points 
  -u [ --update-descriptors ] arg frequency (iterations) to update descriptors 
                                  of regions (0=no update)
  --adaptative-descriptors        recomputes descriptors more often at the 
                                  beginning of the process
  --step-auto                     guess appropriate step size depending on 
                                  first iteration
  --convergence-energy            disables lazy convergence tracking: instead 
                                  of fast computation of the mean norm of the 
                                  displacement field, it computes the full 
                                  energy functional

Functional options (by levels):
  --smoothing arg               apply isotropic smoothing filter on target 
                                image, with kernel sigma=S mm.
  --smooth-auto                 apply isotropic smoothing filter on target 
                                image, with automatic computation of kernel 
                                sigma.
  --uniform-bg-membership       consider last ROI as background and do not 
                                compute descriptors.
  -d [ --decile-threshold ] arg set (decile) threshold to consider a computed 
                                gradient as outlier (ranges 0.0-0.5)