FIX: Segmentation plots aligned with cardinal axes

niworkflows/utils/images.py

@@ -3,6 +3,28 @@
 import numpy as np
 
 
+def rotation2canonical(img):
+    """Calculate the rotation w.r.t. cardinal axes of input image."""
+    img = nb.as_closest_canonical(img)
+    newaff = np.diag(img.header.get_zooms()[:3])
+    r = newaff @ np.linalg.inv(img.affine[:3, :3])
+    if np.allclose(r, np.eye(3)):
+        return None
+    return r
+
+
+def rotate_affine(img, rot=None):
+    """Rewrite the affine of a spatial image."""
+    if rot is None:
+        return img
+
+    img = nb.as_closest_canonical(img)
+    affine = np.eye(4)
+    affine[:3, :3] = rot @ img.affine[:3, :3]
+    affine[:3, 3] = rot @ img.affine[:3, 3]
+    return img.__class__(img.dataobj, affine, img.header)
+
+
 def unsafe_write_nifti_header_and_data(fname, header, data):
     """Write header and data without any consistency checks or data munging
 

niworkflows/viz/utils.py

@@ -20,6 +20,7 @@
 
 from nipype.utils import filemanip
 from .. import NIWORKFLOWS_LOG
+from ..utils.images import rotation2canonical, rotate_affine
 
 
 SVGNS = "http://www.w3.org/2000/svg"
@@ -216,19 +217,29 @@ def plot_segs(
     compress="auto",
     **plot_params
 ):
-    """ plot segmentation as contours over the image (e.g. anatomical).
+    """
+    Generate a static mosaic with ROIs represented by their delimiting contour.
+
+    Plot segmentation as contours over the image (e.g. anatomical).
     seg_niis should be a list of files. mask_nii helps determine the cut
     coordinates. plot_params will be passed on to nilearn plot_* functions. If
     seg_niis is a list of size one, it behaves as if it was plotting the mask.
     """
     plot_params = {} if plot_params is None else plot_params
 
     image_nii = _3d_in_file(image_nii)
+    canonical_r = rotation2canonical(image_nii)
+    image_nii = rotate_affine(image_nii, rot=canonical_r)
+    seg_niis = [rotate_affine(_3d_in_file(f), rot=canonical_r) for f in seg_niis]
     data = image_nii.get_fdata()
 
     plot_params = robust_set_limits(data, plot_params)
 
-    bbox_nii = nb.load(image_nii if bbox_nii is None else bbox_nii)
+    bbox_nii = (
+        image_nii if bbox_nii is None
+        else rotate_affine(_3d_in_file(bbox_nii), rot=canonical_r)
+    )
+
     if masked:
         bbox_nii = nlimage.threshold_img(bbox_nii, 1e-3)