Add options to skip operations for RestoreLabeld Transform (#8125)

Fixes #6380 

### Description

Four new bool parameters are added into `RestoreLabeld` to allow users
to selectively enable or disable each restoration operation as needed,
and a corresponding test case is added to verify that the function runs
correctly.

This design allows users to selectively enable or disable each
restoration operation as needed, providing greater flexibility.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Hsin Tong <[email protected]>
Signed-off-by: Hsin-Tong Hsieh <[email protected]>
Signed-off-by: kbbbbkb <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Kerfoot <[email protected]>
Co-authored-by: kbbbbkb <[email protected]>

Author: 4 people

monai/apps/deepgrow/transforms.py

@@ -803,6 +803,14 @@ class RestoreLabeld(MapTransform):
         original_shape_key: key that records original shape for foreground.
         cropped_shape_key: key that records cropped shape for foreground.
         allow_missing_keys: don't raise exception if key is missing.
+        restore_resizing: used to enable or disable resizing restoration, default is True.
+            If True, the transform will resize the items back to its original shape.
+        restore_cropping: used to enable or disable cropping restoration, default is True.
+            If True, the transform will restore the items to its uncropped size.
+        restore_spacing: used to enable or disable spacing restoration, default is True.
+            If True, the transform will resample the items back to the spacing it had before being altered.
+        restore_slicing: used to enable or disable slicing restoration, default is True.
+            If True, the transform will reassemble the full volume by restoring the slices to their original positions.
     """
 
     def __init__(
@@ -819,6 +827,10 @@ def __init__(
         original_shape_key: str = "foreground_original_shape",
         cropped_shape_key: str = "foreground_cropped_shape",
         allow_missing_keys: bool = False,
+        restore_resizing: bool = True,
+        restore_cropping: bool = True,
+        restore_spacing: bool = True,
+        restore_slicing: bool = True,
     ) -> None:
         super().__init__(keys, allow_missing_keys)
         self.ref_image = ref_image
@@ -833,6 +845,10 @@ def __init__(
         self.end_coord_key = end_coord_key
         self.original_shape_key = original_shape_key
         self.cropped_shape_key = cropped_shape_key
+        self.restore_resizing = restore_resizing
+        self.restore_cropping = restore_cropping
+        self.restore_spacing = restore_spacing
+        self.restore_slicing = restore_slicing
 
     def __call__(self, data: Any) -> dict:
         d = dict(data)
@@ -842,38 +858,45 @@ def __call__(self, data: Any) -> dict:
             image = d[key]
 
             # Undo Resize
-            current_shape = image.shape
-            cropped_shape = meta_dict[self.cropped_shape_key]
-            if np.any(np.not_equal(current_shape, cropped_shape)):
-                resizer = Resize(spatial_size=cropped_shape[1:], mode=mode)
-                image = resizer(image, mode=mode, align_corners=align_corners)
+            if self.restore_resizing:
+                current_shape = image.shape
+                cropped_shape = meta_dict[self.cropped_shape_key]
+                if np.any(np.not_equal(current_shape, cropped_shape)):
+                    resizer = Resize(spatial_size=cropped_shape[1:], mode=mode)
+                    image = resizer(image, mode=mode, align_corners=align_corners)
 
             # Undo Crop
-            original_shape = meta_dict[self.original_shape_key]
-            result = np.zeros(original_shape, dtype=np.float32)
-            box_start = meta_dict[self.start_coord_key]
-            box_end = meta_dict[self.end_coord_key]
-
-            spatial_dims = min(len(box_start), len(image.shape[1:]))
-            slices = tuple(
-                [slice(None)] + [slice(s, e) for s, e in zip(box_start[:spatial_dims], box_end[:spatial_dims])]
-            )
-            result[slices] = image
+            if self.restore_cropping:
+                original_shape = meta_dict[self.original_shape_key]
+                result = np.zeros(original_shape, dtype=np.float32)
+                box_start = meta_dict[self.start_coord_key]
+                box_end = meta_dict[self.end_coord_key]
+
+                spatial_dims = min(len(box_start), len(image.shape[1:]))
+                slices = tuple(
+                    [slice(None)] + [slice(s, e) for s, e in zip(box_start[:spatial_dims], box_end[:spatial_dims])]
+                )
+                result[slices] = image
+            else:
+                result = image
 
             # Undo Spacing
-            current_size = result.shape[1:]
-            # change spatial_shape from HWD to DHW
-            spatial_shape = list(np.roll(meta_dict["spatial_shape"], 1))
-            spatial_size = spatial_shape[-len(current_size) :]
+            if self.restore_spacing:
+                current_size = result.shape[1:]
+                # change spatial_shape from HWD to DHW
+                spatial_shape = list(np.roll(meta_dict["spatial_shape"], 1))
+                spatial_size = spatial_shape[-len(current_size) :]
 
-            if np.any(np.not_equal(current_size, spatial_size)):
-                resizer = Resize(spatial_size=spatial_size, mode=mode)
-                result = resizer(result, mode=mode, align_corners=align_corners)  # type: ignore
+                if np.any(np.not_equal(current_size, spatial_size)):
+                    resizer = Resize(spatial_size=spatial_size, mode=mode)
+                    result = resizer(result, mode=mode, align_corners=align_corners)  # type: ignore
 
             # Undo Slicing
             slice_idx = meta_dict.get("slice_idx")
             final_result: NdarrayOrTensor
-            if slice_idx is None or self.slice_only:
+            if not self.restore_slicing:  # do nothing if restore slicing isn't requested
+                final_result = result
+            elif slice_idx is None or self.slice_only:
                 final_result = result if len(result.shape) <= 3 else result[0]
             else:
                 slice_idx = meta_dict["slice_idx"][0]

tests/test_deepgrow_transforms.py

@@ -141,6 +141,21 @@
 
 DATA_12 = {"image": np.arange(27).reshape(3, 3, 3), PostFix.meta("image"): {}, "guidance": [[0, 0, 0], [0, 1, 1], 1]}
 
+DATA_13 = {
+    "image": np.arange(64).reshape((1, 4, 4, 4)),
+    PostFix.meta("image"): {
+        "spatial_shape": [8, 8, 4],
+        "foreground_start_coord": np.array([1, 1, 1]),
+        "foreground_end_coord": np.array([3, 3, 3]),
+        "foreground_original_shape": (1, 4, 4, 4),
+        "foreground_cropped_shape": (1, 2, 2, 2),
+        "original_affine": np.array(
+            [[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]]
+        ),
+    },
+    "pred": np.array([[[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]),
+}
+
 FIND_SLICE_TEST_CASE_1 = [{"label": "label", "sids": "sids"}, DATA_1, [0]]
 
 FIND_SLICE_TEST_CASE_2 = [{"label": "label", "sids": "sids"}, DATA_2, [0, 1]]
@@ -329,6 +344,74 @@
 
 RESTORE_LABEL_TEST_CASE_2 = [{"keys": ["pred"], "ref_image": "image", "mode": "nearest"}, DATA_11, RESULT]
 
+RESTORE_LABEL_TEST_CASE_3_RESULT = np.zeros((10, 20, 20))
+RESTORE_LABEL_TEST_CASE_3_RESULT[:5, 0:10, 0:10] = 1
+RESTORE_LABEL_TEST_CASE_3_RESULT[:5, 0:10, 10:20] = 2
+RESTORE_LABEL_TEST_CASE_3_RESULT[:5, 10:20, 0:10] = 3
+RESTORE_LABEL_TEST_CASE_3_RESULT[:5, 10:20, 10:20] = 4
+RESTORE_LABEL_TEST_CASE_3_RESULT[5:10, 0:10, 0:10] = 5
+RESTORE_LABEL_TEST_CASE_3_RESULT[5:10, 0:10, 10:20] = 6
+RESTORE_LABEL_TEST_CASE_3_RESULT[5:10, 10:20, 0:10] = 7
+RESTORE_LABEL_TEST_CASE_3_RESULT[5:10, 10:20, 10:20] = 8
+
+RESTORE_LABEL_TEST_CASE_3 = [
+    {"keys": ["pred"], "ref_image": "image", "mode": "nearest", "restore_cropping": False},
+    DATA_11,
+    RESTORE_LABEL_TEST_CASE_3_RESULT,
+]
+
+RESTORE_LABEL_TEST_CASE_4_RESULT = np.zeros((4, 8, 8))
+RESTORE_LABEL_TEST_CASE_4_RESULT[1, 2:6, 2:6] = np.array(
+    [[10.0, 10.0, 20.0, 20.0], [10.0, 10.0, 20.0, 20.0], [30.0, 30.0, 40.0, 40.0], [30.0, 30.0, 40.0, 40.0]]
+)
+RESTORE_LABEL_TEST_CASE_4_RESULT[2, 2:6, 2:6] = np.array(
+    [[50.0, 50.0, 60.0, 60.0], [50.0, 50.0, 60.0, 60.0], [70.0, 70.0, 80.0, 80.0], [70.0, 70.0, 80.0, 80.0]]
+)
+
+RESTORE_LABEL_TEST_CASE_4 = [
+    {"keys": ["pred"], "ref_image": "image", "mode": "nearest", "restore_resizing": False},
+    DATA_13,
+    RESTORE_LABEL_TEST_CASE_4_RESULT,
+]
+
+RESTORE_LABEL_TEST_CASE_5_RESULT = np.zeros((4, 4, 4))
+RESTORE_LABEL_TEST_CASE_5_RESULT[1, 1:3, 1:3] = np.array([[10.0, 20.0], [30.0, 40.0]])
+RESTORE_LABEL_TEST_CASE_5_RESULT[2, 1:3, 1:3] = np.array([[50.0, 60.0], [70.0, 80.0]])
+
+RESTORE_LABEL_TEST_CASE_5 = [
+    {"keys": ["pred"], "ref_image": "image", "mode": "nearest", "restore_spacing": False},
+    DATA_13,
+    RESTORE_LABEL_TEST_CASE_5_RESULT,
+]
+
+RESTORE_LABEL_TEST_CASE_6_RESULT = np.zeros((1, 4, 8, 8))
+RESTORE_LABEL_TEST_CASE_6_RESULT[-1, 1, 2:6, 2:6] = np.array(
+    [[10.0, 10.0, 20.0, 20.0], [10.0, 10.0, 20.0, 20.0], [30.0, 30.0, 40.0, 40.0], [30.0, 30.0, 40.0, 40.0]]
+)
+RESTORE_LABEL_TEST_CASE_6_RESULT[-1, 2, 2:6, 2:6] = np.array(
+    [[50.0, 50.0, 60.0, 60.0], [50.0, 50.0, 60.0, 60.0], [70.0, 70.0, 80.0, 80.0], [70.0, 70.0, 80.0, 80.0]]
+)
+
+RESTORE_LABEL_TEST_CASE_6 = [
+    {"keys": ["pred"], "ref_image": "image", "mode": "nearest", "restore_slicing": False},
+    DATA_13,
+    RESTORE_LABEL_TEST_CASE_6_RESULT,
+]
+
+RESTORE_LABEL_TEST_CASE_7 = [
+    {
+        "keys": ["pred"],
+        "ref_image": "image",
+        "mode": "nearest",
+        "restore_resizing": False,
+        "restore_cropping": False,
+        "restore_spacing": False,
+        "restore_slicing": False,
+    },
+    DATA_11,
+    np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]),
+]
+
 FETCH_2D_SLICE_TEST_CASE_1 = [
     {"keys": ["image"], "guidance": "guidance"},
     DATA_12,
@@ -445,7 +528,17 @@ def test_correct_results(self, arguments, input_data, expected_result):
 
 class TestRestoreLabeld(unittest.TestCase):
 
-    @parameterized.expand([RESTORE_LABEL_TEST_CASE_1, RESTORE_LABEL_TEST_CASE_2])
+    @parameterized.expand(
+        [
+            RESTORE_LABEL_TEST_CASE_1,
+            RESTORE_LABEL_TEST_CASE_2,
+            RESTORE_LABEL_TEST_CASE_3,
+            RESTORE_LABEL_TEST_CASE_4,
+            RESTORE_LABEL_TEST_CASE_5,
+            RESTORE_LABEL_TEST_CASE_6,
+            RESTORE_LABEL_TEST_CASE_7,
+        ]
+    )
     def test_correct_results(self, arguments, input_data, expected_result):
         result = RestoreLabeld(**arguments)(input_data)
         np.testing.assert_allclose(result["pred"], expected_result)