Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fix EchoMeasure's myo_thickness method to not depend on receiving control points in clockwise order #184

Merged
merged 6 commits into from
Nov 8, 2023
Merged

Fix EchoMeasure's myo_thickness method to not depend on receiving control points in clockwise order #184

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
d2b2b12
Define new attributes to extract from images
nathanpainchaud Nov 8, 2023
06e1e97
Standardized return value units for previous attributes
nathanpainchaud Nov 8, 2023
260803b
Test for the curvature attribute on a circle of known radius
nathanpainchaud Nov 8, 2023
edb4deb
Test for the myo thickness attribute on concentric circles of known r…
nathanpainchaud Nov 8, 2023
8e8eb9e
Fix myo thickness to not depend on clockwise order of control points
nathanpainchaud Nov 8, 2023
da9d854
Merge branch 'dev' into feature/new-echo-measures
nathanpainchaud Nov 8, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 12 additions & 6 deletions vital/utils/image/us/measure.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -616,8 +616,14 @@ def myo_thickness(
# Sample points every mm (up to 1.2cm) along the orthogonal vectors towards both the endo and epi contours
centerline = centerline_pix * voxelspacing # (num_control_points, 2)
sample_offsets = unit_orth_vecs[:, None, :] * np.arange(1, 13)[None, :, None] # (num_control_points, 12, 2)
centerline_inner_samples = centerline[:, None] + sample_offsets # (num_control_points, 12, 2)
centerline_outer_samples = centerline[:, None] - sample_offsets # (num_control_points, 12, 2)
centerline_orth_segments = np.concatenate(
[
centerline[:, None] - sample_offsets, # (num_control_points, 12, 2)
centerline[:, None], # (num_control_points, 1, 2)
centerline[:, None] + sample_offsets, # (num_control_points, 12, 2)
],
axis=1,
) # (num_control_points, 25, 2)

# Extract the endo and epi contours, in both pixel and physical coordinates
endo_contour_pix, epi_contour_pix = [
Expand All @@ -627,14 +633,14 @@ def myo_thickness(
endo_contour, epi_contour = endo_contour_pix * voxelspacing, epi_contour_pix * voxelspacing

# For each centerline point, find the nearest point on the endo and epi contours to the points sampled along the
# orthogonal vector
# orthogonal segment
endo_closest_orth, epi_closest_orth = [], []
for inner_samples, outer_samples in zip(centerline_inner_samples, centerline_outer_samples):
endo_dist = distance.cdist(inner_samples, endo_contour) # (12, `len(endo_contour)`)
for orth_segment in centerline_orth_segments:
endo_dist = distance.cdist(orth_segment, endo_contour) # (25, `len(endo_contour)`)
closest_endo_idx = np.unravel_index(np.argmin(endo_dist), endo_dist.shape)[1]
endo_closest_orth.append(endo_contour_pix[closest_endo_idx])

epi_dist = distance.cdist(outer_samples, epi_contour) # (12, `len(epi_contour)`)
epi_dist = distance.cdist(orth_segment, epi_contour) # (25, `len(epi_contour)`)
closest_epi_idx = np.unravel_index(np.argmin(epi_dist), epi_dist.shape)[1]
epi_closest_orth.append(epi_contour_pix[closest_epi_idx])

Expand Down