jlf is a module that uses the ANTs Joint Label Fusion algorithm to produce a
high-resolution anatomical segmentation of the subject’s anatomical data. Generates a subject-specific atlas of anatomical landmarks that can be used for regional quantification or network mapping. Presently, the module uses atlases of 103-OASIS labels.

jlf options

If to use OASIS atlas labels with skullstrip or not:

- jlf_extract[cxt]=1 # with skulltrip

If to keep each warped atlas, that is not advisable, it occupy space:

- jlf_keep_warps[cxt]=0 # dont keep

Fast joint label fusion is no recommended bcos of poor accuracy:

- jlf_quick[cxt]=1 # for fast jlf but no recommended

The cohort of OASIS label can be selected based on their ages:

- jlf_cohort[cxt]=All # Everyone
- jlf_cohort[cxt]=YoungAdult22  # Age range of 18-34
- jlf_cohort[cxt]=Older18  # Age range of 23-90
- jlf_cohort[cxt]=SexBalanced20  #All male subjects (ages 20-68) plus 10 of the female subjects.
- jlf_cohort[cxt]=Subset24 # A subset for general use, slightly more balanced on sex
- jlf_cohort[cxt]=Younger24 #Maintains the same 2:1 female:male ratio of the original, but biased towards younger subjects

Tthe number of cpu cores, the default is 2:

- jlf_ncpu[cxt]=2

Configuring parallelisation,very fast:

- jlf_parallel[3]=1


The expected outputs are:

- prefix_Intensity.nii.gz # atlas intensity
- prefix_Labels.nii.gz # atlas labels
- prefix_TargetMaskImageOr.nii.gz # target mask that cover all atlas
- prefix_LabelsGMIntersect.nii.gz # refined atlas with grey matter mask