Merge pull request #138 from khanlab/clean-repo

removes deprecated and unused files

docs/getting_started/installation.rst

@@ -17,11 +17,8 @@ Notes:
 ^^^^^^
 
 * T1w and/or T2w input images are supported, and we recommend using sub-millimetric isotropic data for best performance.
-
 * Other 3D imaging modalities (eg. ex-vivo MRI, 3D histology, etc.) can be used, but may require manual tissue segmentation.
 
-* DWI workflows are also available but currently experimental.
-
 
 
 Running with Docker

hippunfold/config/nnunet_model_urls.yml

@@ -1,7 +1,3 @@
-
 T1w: 'https://zenodo.org/record/4508747/files/trained_model.3d_fullres.Task101_hcp1200_T1w.nnUNetTrainerV2.model_best.tar'
 T2w: 'https://zenodo.org/record/4508747/files/trained_model.3d_fullres.Task102_hcp1200_T2w.nnUNetTrainerV2.model_best.tar'
-T1T2w: 'https://zenodo.org/record/4508747/files/trained_model.3d_fullres.Task103_hcp1200_T1T2w.nnUNetTrainerV2.model_best.tar'
-b1000: 'https://zenodo.org/record/4508747/files/trained_model.3d_fullres.Task104_hcp1200_b1000.nnUNetTrainerV2.model_best.tar'
-trimodal: 'https://zenodo.org/record/4508747/files/trained_model.3d_fullres.Task105_hcp1200_trimodal.nnUNetTrainerV2.model_best.tar'
 hippb500: 'https://zenodo.org/record/5732291/files/trained_model.3d_fullres.Task110_hcp1200_b1000crop.nnUNetTrainerV2.model_best.tar'

hippunfold/config/snakebids.yml

@@ -41,19 +41,6 @@ pybids_inputs:
       - acquisition
       - run
 
-  dwi:
-    filters:
-      suffix: 'dwi'
-      extension: '.nii.gz'
-      invalid_filters: 'allow'
-      datatype: 'dwi'
-    wildcards:
-      - subject
-      - session
-      - acquisition
-      - run
-      - dir
-
   hippb500:
     filters:
       suffix: 'b500'
@@ -144,7 +131,6 @@ parse_args:
       - T1w
       - T2w
       - hippb500
-      - dwi
       - segT1w
       - segT2w
       - cropseg
@@ -271,20 +257,9 @@ modality:
 nnunet_model:
   T1w: trained_model.3d_fullres.Task101_hcp1200_T1w.nnUNetTrainerV2.model_best.tar
   T2w: trained_model.3d_fullres.Task102_hcp1200_T2w.nnUNetTrainerV2.model_best.tar
-  T1T2w: trained_model.3d_fullres.Task103_hcp1200_T1T2w.nnUNetTrainerV2.model_best.tar
-  b1000: trained_model.3d_fullres.Task104_hcp1200_b1000.nnUNetTrainerV2.model_best.tar
-  trimodal: trained_model.3d_fullres.Task105_hcp1200_trimodal.nnUNetTrainerV2.model_best.tar
   hippb500: trained_model.3d_fullres.Task110_hcp1200_b1000crop.nnUNetTrainerV2.model_best.tar
 
 
-#niftynet models -- this will be deprecated soon:
-cnn_model:
-  T2w: 'HCP1200-T2'
-  T1w: 'HCP1200-T1'
-  hippb500: 'HCP1200-b1000'
-  b1000: 'HCP1200-b1000'
-
-
 hippdwi_opts:
   resample_dim: '734x720x67' # from 220x216x20 @ 1x1x1mm -> 0.3mm
   bbox_x:
@@ -385,35 +360,3 @@ skip_coreg: False
 keep_work: False
 skip_inject_template_labels: False
 
-
-#---- dwi options below, not used in the default workflow ----
-
-#eddy options only used for experimental dwi workflow
-eddy:
-  flags:
-    verbose: True
-    repol: True
-    cnr_maps: True
-    residuals: True
-    data_is_shelled: True
-  opts:
-    mporder: 6
-    s2v_niter: 5
-    s2v_lambda: 1
-    s2v_interp: trilinear
-    ol_type: 'both'  #can be sw, gw, or both (use sw if no multi-band)
-
-#masking options for dwi
-masking:
-  default_method: 'bet_from-b0_frac-1' #b0SyN_from-MNI152NLin2009cSym' #default method to use for brainmasking
-  methods: # list of methods (for QC report)
-    - 'bet_from-b0'
-    - 'bet_from-b0_frac-4'
-    - 'b0SyN_from-MNI152NLin2009cSym'
-  import_path: #only required if the import_mask - not implemented yet..
-    - '/path/to/mask_to_import/sub-{subject}_mask.nii.gz'
-  custom: #use this to set a custom method for a particular subject
-    #'0015': 'bet_from-b0_frac-4'
-    '0015': 'b0SyN_from-MNI152NLin2009cSym'
-
-archive_work: False

hippunfold/workflow/Snakefile

@@ -62,8 +62,6 @@ else:
 if 'segT2w' in config['modality'] or 'T2w' in config['modality']:
     include: 'rules/preproc_t2.smk'
 
-if 'dwi' in config['modality']:
-    include: 'rules/preproc_dwi.smk'
 
 if 'hippb500' in config['modality']:
     include: 'rules/preproc_hippb500.smk'