FIX: Revert to FAST for tissue probability maps

smriprep/workflows/anatomical.py

@@ -475,38 +475,39 @@ def _check_img(img):
     ])
     # fmt:on
 
-    if not freesurfer:  # Flag --fs-no-reconall is set - return
-        # Brain tissue segmentation - FAST produces: 0 (bg), 1 (wm), 2 (csf), 3 (gm)
-        t1w_dseg = pe.Node(
-            fsl.FAST(segments=True, no_bias=True, probability_maps=True),
-            name="t1w_dseg",
-            mem_gb=3,
-        )
-        lut_t1w_dseg.inputs.lut = (0, 3, 1, 2)  # Maps: 0 -> 0, 3 -> 1, 1 -> 2, 2 -> 3.
-        fast2bids = pe.Node(
-            niu.Function(function=_probseg_fast2bids),
-            name="fast2bids",
-            run_without_submitting=True,
-        )
+    # XXX Keeping FAST separate so that it's easier to swap in ANTs or FreeSurfer
 
+    # Brain tissue segmentation - FAST produces: 0 (bg), 1 (wm), 2 (csf), 3 (gm)
+    t1w_dseg = pe.Node(
+        fsl.FAST(segments=True, no_bias=True, probability_maps=True),
+        name="t1w_dseg",
+        mem_gb=3,
+    )
+    lut_t1w_dseg.inputs.lut = (0, 3, 1, 2)  # Maps: 0 -> 0, 3 -> 1, 1 -> 2, 2 -> 3.
+    fast2bids = pe.Node(
+        niu.Function(function=_probseg_fast2bids),
+        name="fast2bids",
+        run_without_submitting=True,
+    )
+
+    # fmt:off
+    workflow.connect([
+        (buffernode, t1w_dseg, [('t1w_brain', 'in_files')]),
+        (t1w_dseg, lut_t1w_dseg, [('partial_volume_map', 'in_dseg')]),
+        (t1w_dseg, fast2bids, [('partial_volume_files', 'inlist')]),
+        (fast2bids, anat_norm_wf, [('out', 'inputnode.moving_tpms')]),
+        (fast2bids, outputnode, [('out', 't1w_tpms')]),
+    ])
+    # fmt:on
+    if not freesurfer:  # Flag --fs-no-reconall is set - return
         # fmt:off
         workflow.connect([
             (brain_extraction_wf, buffernode, [
                 (('outputnode.out_file', _pop), 't1w_brain'),
                 ('outputnode.out_mask', 't1w_mask')]),
-            (buffernode, t1w_dseg, [('t1w_brain', 'in_files')]),
-            (t1w_dseg, lut_t1w_dseg, [('partial_volume_map', 'in_dseg')]),
-            (t1w_dseg, fast2bids, [('partial_volume_files', 'inlist')]),
-            (fast2bids, anat_norm_wf, [('out', 'inputnode.moving_tpms')]),
-            (fast2bids, outputnode, [('out', 't1w_tpms')]),
         ])
-        # fmt:on
         return workflow
 
-    # Map FS' aseg labels onto three-tissue segmentation
-    lut_t1w_dseg.inputs.lut = _aseg_to_three()
-    split_seg = pe.Node(niu.Function(function=_split_segments), name="split_seg")
-
     # check for older IsRunning files and remove accordingly
     fs_isrunning = pe.Node(
         niu.Function(function=_fs_isRunning), overwrite=True, name="fs_isrunning"
@@ -537,12 +538,6 @@ def _check_img(img):
             (('outputnode.bias_corrected', _pop), 'in_file')]),
         (surface_recon_wf, applyrefined, [
             ('outputnode.out_brainmask', 'mask_file')]),
-        (surface_recon_wf, lut_t1w_dseg, [
-            ('outputnode.out_aseg', 'in_dseg')]),
-        (lut_t1w_dseg, split_seg, [('out', 'in_file')]),
-        (split_seg, anat_norm_wf, [
-            ('out', 'inputnode.moving_tpms')]),
-        (split_seg, outputnode, [('out', 't1w_tpms')]),
         (surface_recon_wf, outputnode, [
             ('outputnode.subjects_dir', 'subjects_dir'),
             ('outputnode.subject_id', 'subject_id'),
@@ -742,70 +737,6 @@ def _pop(inlist):
     return inlist
 
 
-def _aseg_to_three():
-    """
-    Map FreeSurfer's segmentation onto a brain (3-)tissue segmentation.
-
-    This function generates an index of 255+0 labels and maps them into zero (bg),
-    1 (GM), 2 (WM), or 3 (CSF). The new values are set according to BIDS-Derivatives.
-    Then the index is populated (e.g., label 3 in the original segmentation maps to label
-    1 in the output).
-    The `aseg lookup table
-    <https://github.com/freesurfer/freesurfer/blob/2beb96c6099d96508246c14a24136863124566a3/distribution/ASegStatsLUT.txt>`__
-    is available in the FreeSurfer source.
-
-    """
-    import numpy as np
-
-    # Base struct
-    aseg_lut = np.zeros((256,), dtype="int")
-    # GM
-    aseg_lut[3] = 1
-    aseg_lut[8:14] = 1
-    aseg_lut[17:21] = 1
-    aseg_lut[26:40] = 1
-    aseg_lut[42] = 1
-    aseg_lut[47:73] = 1
-
-    # CSF
-    aseg_lut[4:6] = 3
-    aseg_lut[14:16] = 3
-    aseg_lut[24] = 3
-    aseg_lut[43:45] = 3
-    aseg_lut[72] = 3
-
-    # WM
-    aseg_lut[2] = 2
-    aseg_lut[7] = 2
-    aseg_lut[16] = 2
-    aseg_lut[28] = 2
-    aseg_lut[41] = 2
-    aseg_lut[46] = 2
-    aseg_lut[60] = 2
-    aseg_lut[77:80] = 2
-    aseg_lut[250:256] = 2
-    return tuple(aseg_lut)
-
-
-def _split_segments(in_file):
-    from pathlib import Path
-    import numpy as np
-    import nibabel as nb
-
-    segimg = nb.load(in_file)
-    data = np.int16(segimg.dataobj)
-    hdr = segimg.header.copy()
-    hdr.set_data_dtype("uint8")
-
-    out_files = []
-    for i, label in enumerate(("GM", "WM", "CSF"), 1):
-        out_fname = str(Path.cwd() / f"aseg_label-{label}_mask.nii.gz")
-        segimg.__class__(data == i, segimg.affine, hdr).to_filename(out_fname)
-        out_files.append(out_fname)
-
-    return out_files
-
-
 def _probseg_fast2bids(inlist):
     """Reorder a list of probseg maps from FAST (CSF, WM, GM) to BIDS (GM, WM, CSF)."""
     return (inlist[1], inlist[2], inlist[0])