Evaluation
Evaluation functions for registration quality assessment
- core.evaluation.evaluation.apply_displacement_field_to_points(points, displacement_field, pixel_scale=1.0)[source]
Apply displacement field to a set of points
- Parameters:
points – Input points (N, 2)
displacement_field – Displacement field (H, W, 2)
pixel_scale – Scale factor to convert points to pixel coordinates
- Returns:
(transformed_points, valid_mask)
- Return type:
- core.evaluation.evaluation.evaluate_nonrigid_registration(fixed_points, moving_points, rigid_transform, displacement_field, pixel_scale=16)[source]
Evaluate non-rigid registration with displacement field
- Parameters:
fixed_points – Fixed landmark points
moving_points – Moving landmark points
rigid_transform – Rigid transformation matrix
displacement_field – Non-rigid displacement field
pixel_scale – Scale factor for pixel conversion
- Returns:
Evaluation metrics
- Return type:
- core.evaluation.evaluation.evaluate_registration_tre(fixed_points, moving_points, transform_matrix, target_shape, scale_factor=None)[source]
Evaluate registration using TRE metrics
- Parameters:
fixed_points – Fixed landmark points
moving_points – Moving landmark points
transform_matrix – Transformation matrix to apply
target_shape – Shape of target image for rTRE calculation
scale_factor – Optional scaling factor for transform
- Returns:
Dictionary with TRE metrics
- Return type:
- core.evaluation.evaluation.load_evaluation_landmarks(fixed_path, moving_path, scale_factor=1000)[source]
Load evaluation landmark points (alternative format)
- Parameters:
fixed_path – Path to fixed landmarks CSV
moving_path – Path to moving landmarks CSV
scale_factor – Scaling factor for coordinates
- Returns:
(fixed_points, moving_points)
- Return type:
- core.evaluation.evaluation.load_landmark_points(fixed_path, moving_path, scale_factor=1.0)[source]
Load landmark points from CSV files
- Parameters:
fixed_path – Path to fixed landmarks CSV
moving_path – Path to moving landmarks CSV
scale_factor – Scaling factor for coordinates
- Returns:
(fixed_points, moving_points)
- Return type:
- core.evaluation.evaluation.ngf_metric(fixed_image, moving_image, epsilon=0.01)[source]
Calculate Normalized Gradient Field (NGF) metric. Works well for multi-stain registration as it focuses on edge alignment.
- Parameters:
fixed_image – Fixed image array (H, W, C)
moving_image – Moving image array (H, W, C)
epsilon – Small constant to avoid division by zero
- Returns:
NGF metric (float)
- core.evaluation.evaluation.rtre(landmarks_1, landmarks_2, x_size, y_size)[source]
Calculate relative Target Registration Error (rTRE)
- Parameters:
landmarks_1 – First set of landmarks (N, 2)
landmarks_2 – Second set of landmarks (N, 2)
x_size – Image width
y_size – Image height
- Returns:
Relative TRE values