removed segT*w

.github/workflows/python-testing.yml

@@ -94,17 +94,13 @@ jobs:
         run: |
           poetry run hippunfold test_data/bids_singleT2w_longitudinal test_out participant -np --modality T2w
 
-      - name: Test manual seg T2w bids
-        run: |
-          poetry run hippunfold test_data/bids_segT2w test_out participant -np --modality segT2w
-
       - name: Test cropseg bids, with path override
         run: |
-          poetry run hippunfold . test_out participant -np --modality cropseg --path_cropseg test_data/data_cropseg/sub-{subject}_hemi-{hemi}_dseg.nii.gz
+          poetry run hippunfold test_data/data_cropseg test_out participant -np --modality cropseg --path_cropseg test_data/data_cropseg/sub-{subject}_hemi-{hemi}_dseg.nii.gz
 
-      - name: Test cropseg bids, with path override, left hemi
+      - name: Test cropseg bids, one hemisphere hemi
         run: |
-          poetry run hippunfold . test_out participant -np --modality cropseg --path_cropseg test_data/data_cropseg_1hemi/sub-{subject}_hemi-{hemi}_dseg.nii.gz --hemi L
+          poetry run hippunfold test_data/data_cropseg_1hemi test_out participant -np --modality cropseg --hemi L
 
       - name: Test T2w with T1w template registration
         run: |

hippunfold/config/snakebids.yml

@@ -67,18 +67,6 @@ pybids_inputs:
       - acquisition
       - run
 
-  seg:
-    filters:
-      suffix: 'dseg'
-      extension: '.nii.gz'
-      datatype: 'anat'
-      invalid_filters: 'allow'
-    wildcards:
-      - subject
-      - session
-      - acquisition
-      - run
-
   cropseg:
     filters:
       suffix: 'dseg'
@@ -100,14 +88,12 @@ pybids_inputs:
 parse_args:
 
   --modality:
-    help: 'Type of image to run hippunfold on. Modality prefixed with seg will import an existing (manual) hippocampal tissue segmentation from that space, instead of running neural network (default: %(default)s)'
+    help: 'Type of image to run hippunfold on. For manual segmentations, use cropseg and be sure to match the label scheme applied in the look up table (lut) online. For best performance, please also make sure the input is approximately aligned to the template space, with hemi-L|R in the name. (default: %(default)s)'
     required: True
     choices:
       - T1w
       - T2w
       - hippb500
-      - segT1w
-      - segT2w
       - cropseg
 
 

hippunfold/resources/label_lut/manual_dseg.tsv

@@ -0,0 +1,9 @@
+index	name	abbreviation
+1	hippocampal grey matter	GM
+2	SRLM or 'dark band'	SRLM
+3	neocortex (entorhinal or parahippocampal)	MTLC
+4	pial surface	Pial
+5	hippocampal-amygdalar transition area	HATA
+6	indusium griseum	IndGris
+7	cysts	Cyst
+8   dentate gyrus	DG

hippunfold/resources/label_lut/subfields_dseg .tsv

@@ -0,0 +1,9 @@
+#No.	Label	R	G	B	A
+1	subiculum	41	43	252	0
+2	CA1	102	209	216	0
+3	CA2	0	179	60	0
+4	CA3	255	182	0	0
+5	CA4	255	43	23	0
+6	dentate_gyrus	245	236	0	0
+7	SRLM	119	54	155	0
+8	cysts	255	255	255	0

hippunfold/workflow/Snakefile

@@ -65,13 +65,6 @@ if config["skip_inject_template_labels"]:  # TODO do we need this?
     config["autotop_labels"] = ["hipp"]
 
 
-# add seg (if chosen) and the modality to the inputs
-if config["modality"][:3] == "seg":  # if modality is segT2w, then add seg
-    limit_to_list.add("seg")
-    limit_to_list.add(config["modality"][3:])
-else:
-    limit_to_list.add(config["modality"])
-
 # if atlas doesn't contain alignment features
 if not config["atlas"] == ["multihist7"]:
     config["no_unfolded_reg"] = True
@@ -118,11 +111,7 @@ if "T1w" in limit_to_list:
 
 
 # include rules only as they are needed..
-if config["modality"] == "segT1w" or config["modality"] == "segT2w":
-
-    include: "rules/preproc_seg.smk"
-
-elif config["modality"] == "cropseg":
+if config["modality"] == "cropseg":
 
     include: "rules/preproc_cropseg.smk"
 

hippunfold/workflow/rules/preproc_cropseg.smk

@@ -1,22 +1,30 @@
 
-rule import_cropseg:
+rule import_cropseg_to_corobl:
     input:
         in_img=partial(get_single_bids_input, component="cropseg"),
+        template_dir=Path(download_dir) / "template" / config["template"],
+    params:
+        std_to_cor=lambda wildcards: config["template_files"][config["template"]][
+            "xfm_corobl"
+        ].format(**wildcards),
+        ref=lambda wildcards, input: Path(input.template_dir)
+        / config["template_files"][config["template"]]["crop_ref"].format(**wildcards),
     output:
         nii=bids(
             root=work,
             datatype="anat",
             **inputs.subj_wildcards,
             suffix="dseg.nii.gz",
             space="corobl",
-            hemi="{hemi,L|R}"
+            hemi="{hemi,L|R}",
         ),
-    group:
-        "subj"
     container:
         config["singularity"]["autotop"]
+    group:
+        "subj"
     shell:
-        "cp {input} {output}"
+        "ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS={threads} "
+        "antsApplyTransforms -d 3 --interpolation MultiLabel -i {input.in_img} -o {output.nii} -r {params.ref} -t {params.std_to_cor}"
 
 
 rule lr_flip_seg:

hippunfold/workflow/rules/shape_inject.smk

@@ -41,19 +41,6 @@ def get_input_for_shape_inject(wildcards):
             space="corobl",
             hemi="{hemi}",
         ).format(**wildcards)
-    elif get_modality_key(config["modality"]) == "seg":
-        modality_suffix = get_modality_suffix(config["modality"])
-        seg = (
-            bids(
-                root=work,
-                datatype="anat",
-                **inputs.subj_wildcards,
-                suffix="dseg.nii.gz",
-                space="corobl",
-                hemi="{hemi}",
-                from_="{modality_suffix}",
-            ).format(**wildcards, modality_suffix=modality_suffix),
-        )
     else:
         seg = bids(
             root=work,
@@ -77,18 +64,6 @@ def get_input_splitseg_for_shape_inject(wildcards):
             space="corobl",
             hemi="{hemi}",
         ).format(**wildcards)
-
-    elif get_modality_key(config["modality"]) == "seg":
-        modality_suffix = get_modality_suffix(config["modality"])
-        seg = bids(
-            root=work,
-            datatype="anat",
-            **inputs.subj_wildcards,
-            suffix="dsegsplit",
-            space="corobl",
-            hemi="{hemi}",
-            from_="{modality_suffix}",
-        ).format(**wildcards, modality_suffix=modality_suffix)
     else:
         seg = bids(
             root=work,