Add affine_fliplr option for manual registration

biahub/estimate_registration.py

@@ -44,6 +44,7 @@
 from biahub.register import (
     convert_transform_to_ants,
     convert_transform_to_numpy,
+    get_3D_fliplr_matrix,
     get_3D_rescaling_matrix,
     get_3D_rotation_matrix,
 )
@@ -455,6 +456,7 @@ def user_assisted_registration(
     target_channel_voxel_size: tuple[float, float, float],
     similarity: bool = False,
     pre_affine_90degree_rotation: int = 0,
+    pre_affine_fliplr: bool = False,
 ) -> list[ArrayLike]:
     """
     Perform user-assisted registration of two volumetric image channels.
@@ -481,6 +483,9 @@ def user_assisted_registration(
                          if False, use an Euclidean transform (rotation, translation).
     pre_affine_90degree_rotation : int
         Number of 90-degree rotations to apply to the source channel before registration.
+    pre_affine_fliplr : bool
+        If True, apply left-right flip to the source channel before registration.
+        Note: Flip is applied first, then rotation.
 
     Returns
     -------
@@ -569,7 +574,17 @@ def user_assisted_registration(
         90 * pre_affine_90degree_rotation,
         (target_channel_Z, target_channel_Y, target_channel_X),
     )
-    compound_affine = scaling_affine @ rotate90_affine
+
+    # Apply flip transformation if requested (flip happens first)
+    if pre_affine_fliplr:
+        fliplr_affine = get_3D_fliplr_matrix(
+            (source_channel_Z, source_channel_Y, source_channel_X),
+            (target_channel_Z, target_channel_Y, target_channel_X),
+        )
+    else:
+        fliplr_affine = np.eye(4)
+
+    compound_affine = scaling_affine @ rotate90_affine @ fliplr_affine
     tx_manual = convert_transform_to_ants(compound_affine).invert()
 
     source_zxy_pre_reg = tx_manual.apply_to_image(source_zyx_ants, reference=target_zyx_ants)
@@ -2126,6 +2141,7 @@ def estimate_registration(
                 else False
             ),
             pre_affine_90degree_rotation=settings.manual_registration_settings.affine_90degree_rotation,
+            pre_affine_fliplr=settings.manual_registration_settings.affine_fliplr,
         )
 
     else:

biahub/register.py

@@ -114,6 +114,40 @@ def get_3D_rotation_matrix(
     return rotation_matrix
 
 
+def get_3D_fliplr_matrix(start_shape_zyx: tuple, end_shape_zyx: tuple = None) -> np.ndarray:
+    """
+    Get 3D left-right flip transformation matrix.
+
+    Parameters
+    ----------
+    start_shape_zyx : tuple
+        Shape of the source volume (Z, Y, X).
+    end_shape_zyx : tuple, optional
+        Shape of the target volume (Z, Y, X). If None, uses start_shape_zyx.
+
+    Returns
+    -------
+    np.ndarray
+        4x4 transformation matrix for left-right flip.
+    """
+    center_X_start = start_shape_zyx[-1] / 2
+    if end_shape_zyx is None:
+        center_X_end = center_X_start
+    else:
+        center_X_end = end_shape_zyx[-1] / 2
+
+    # Flip matrix: reflects across X axis and translates to maintain center
+    flip_matrix = np.array(
+        [
+            [1, 0, 0, 0],  # Z unchanged
+            [0, 1, 0, 0],  # Y unchanged
+            [0, 0, -1, 2 * center_X_end],  # X flipped and translated
+            [0, 0, 0, 1],  # Homogeneous coordinate
+        ]
+    )
+    return flip_matrix
+
+
 def convert_transform_to_ants(T_numpy: np.ndarray):
     """Homogeneous 3D transformation matrix from numpy to ants
 

biahub/settings.py

@@ -197,6 +197,7 @@ class AntsRegistrationSettings(MyBaseModel):
 class ManualRegistrationSettings(MyBaseModel):
     time_index: int = 0
     affine_90degree_rotation: int = 0
+    affine_fliplr: bool = False
 
 
 class EstimateRegistrationSettings(MyBaseModel):