[MED-22][external] import nifti to raster layer mask annotations

darwin/importer/formats/nifti.py

@@ -1,4 +1,5 @@
 import sys
+import uuid
 import warnings
 from collections import OrderedDict, defaultdict
 from pathlib import Path
@@ -102,7 +103,7 @@ def _parse_nifti(
             class_img = np.isin(img, class_idxs).astype(np.uint8)
             cc_img, num_labels = cc3d.connected_components(class_img, return_N=True)
             for instance_id in range(1, num_labels):
-                _video_annotations = get_video_annotation(
+                _video_annotations = get_polygon_video_annotations(
                     cc_img,
                     class_idxs=[instance_id],
                     class_name=class_name,
@@ -116,7 +117,7 @@ def _parse_nifti(
         for class_name, class_idxs in processed_class_map.items():
             if class_name == "background":
                 continue
-            _video_annotations = get_video_annotation(
+            _video_annotations = get_polygon_video_annotations(
                 img,
                 class_idxs=class_idxs,
                 class_name=class_name,
@@ -127,10 +128,23 @@ def _parse_nifti(
             if _video_annotations is None:
                 continue
             video_annotations += _video_annotations
-    annotation_classes = {
-        dt.AnnotationClass(class_name, "polygon", "polygon")
-        for class_name in class_map.values()
-    }
+    elif mode == "mask":
+        video_annotations = get_mask_video_annotations(
+            img,
+            processed_class_map,
+            slot_names,
+        )
+    if mode in ["video", "instances"]:
+        annotation_classes = {
+            dt.AnnotationClass(class_name, "polygon", "polygon")
+            for class_name in class_map.values()
+        }
+    elif mode == "mask":
+        annotation_classes = {
+            dt.AnnotationClass(class_name, "mask", "mask")
+            for class_name in class_map.values()
+        }
+
     return dt.AnnotationFile(
         path=json_path,
         filename=str(filename),
@@ -148,7 +162,7 @@ def _parse_nifti(
     )
 
 
-def get_video_annotation(
+def get_polygon_video_annotations(
     volume: np.ndarray,
     class_name: str,
     class_idxs: List[int],
@@ -157,13 +171,18 @@ def get_video_annotation(
     pixdims: Tuple[float],
 ) -> Optional[List[dt.VideoAnnotation]]:
     if not is_mpr:
-        return nifti_to_video_annotation(
-            volume, class_name, class_idxs, slot_names, view_idx=2, pixdims=pixdims
+        return nifti_to_video_polygon_annotation(
+            volume,
+            class_name,
+            class_idxs,
+            slot_names,
+            view_idx=2,
+            pixdims=pixdims,
         )
     elif is_mpr and len(slot_names) == 3:
         video_annotations = []
         for view_idx, slot_name in enumerate(slot_names):
-            _video_annotations = nifti_to_video_annotation(
+            _video_annotations = nifti_to_video_polygon_annotation(
                 volume,
                 class_name,
                 class_idxs,
@@ -177,9 +196,99 @@ def get_video_annotation(
         raise Exception("If is_mpr is True, slot_names must be of length 3")
 
 
-def nifti_to_video_annotation(
-    volume, class_name, class_idxs, slot_names, view_idx=2, pixdims=(1, 1, 1)
-):
+def get_mask_video_annotations(
+    volume: np.ndarray, processed_class_map: Dict, slot_names: List[str]
+) -> Optional[List[dt.VideoAnnotation]]:
+    """
+    The function takes a volume and a class map and returns a list of video annotations
+
+    We write a single raster layer for the volume but K mask annotations, where K is the number of classes.
+    """
+    frame_annotations = OrderedDict()
+    all_mask_annotations = defaultdict(lambda: OrderedDict())
+    # This is a dictionary of class_names to generated mask_annotation_ids
+    mask_annotation_ids = {
+        class_name: str(uuid.uuid4()) for class_name in processed_class_map.keys()
+    }
+    # We need to create a new mapping dictionary where the keys are the mask_annotation_ids
+    # and the values are the new integers which we use in the raster layer
+    mask_annotation_ids_mapping = {}
+    map_from_nifti_idx_to_raster_idx = {0: 0}  # 0 is the background class
+    for i in range(volume.shape[2]):
+        slice_mask = volume[:, :, i].astype(np.uint8)
+        for raster_idx, (class_name, class_idxs) in enumerate(
+            processed_class_map.items()
+        ):
+            if class_name == "background":
+                continue
+            class_mask = np.isin(slice_mask, class_idxs).astype(np.uint8).copy()
+            if class_mask.sum() == 0:
+                continue
+            all_mask_annotations[class_name][i] = dt.make_mask(
+                class_name, subs=None, slot_names=slot_names
+            )
+            all_mask_annotations[class_name][i].id = mask_annotation_ids[class_name]
+            mask_annotation_ids_mapping[mask_annotation_ids[class_name]] = (
+                raster_idx + 1
+            )
+            for class_idx in class_idxs:
+                map_from_nifti_idx_to_raster_idx[class_idx] = raster_idx + 1
+    # Now that we've created all the mask annotations, we need to create the raster layer
+    # We only map the mask_annotation_ids which appear in any given frame.
+    for i in range(volume.shape[2]):
+        slice_mask = volume[:, :, i].astype(
+            np.uint8
+        )  # Product requirement: We only support 255 classes!
+        # We need to convert from nifti_idx to raster_idx
+        slice_mask = np.vectorize(
+            lambda key: map_from_nifti_idx_to_raster_idx.get(key, 0)
+        )(slice_mask)
+        dense_rle = convert_to_dense_rle(slice_mask)
+        raster_annotation = dt.make_raster_layer(
+            class_name="__raster_layer__",
+            mask_annotation_ids_mapping=mask_annotation_ids_mapping,
+            total_pixels=class_mask.size,
+            dense_rle=dense_rle,
+            slot_names=slot_names,
+        )
+        frame_annotations[i] = raster_annotation
+    all_frame_ids = list(frame_annotations.keys())
+    if not all_frame_ids:
+        return None
+    if len(all_frame_ids) == 1:
+        segments = [[all_frame_ids[0], all_frame_ids[0] + 1]]
+    elif len(all_frame_ids) > 1:
+        segments = [[min(all_frame_ids), max(all_frame_ids)]]
+    raster_video_annotation = dt.make_video_annotation(
+        frame_annotations,
+        keyframes={f_id: True for f_id in all_frame_ids},
+        segments=segments,
+        interpolated=False,
+        slot_names=slot_names,
+    )
+    mask_video_annotations = []
+    for class_name, mask_annotations in all_mask_annotations.items():
+        mask_video_annotation = dt.make_video_annotation(
+            mask_annotations,
+            keyframes={f_id: True for f_id in all_frame_ids},
+            segments=segments,
+            interpolated=False,
+            slot_names=slot_names,
+        )
+        mask_video_annotation.id = mask_annotation_ids[class_name]
+        mask_video_annotations.append(mask_video_annotation)
+
+    return [raster_video_annotation] + mask_video_annotations
+
+
+def nifti_to_video_polygon_annotation(
+    volume: np.ndarray,
+    class_name: str,
+    class_idxs: List[int],
+    slot_names: List[str],
+    view_idx: int = 2,
+    pixdims: Tuple[int, int, int] = (1, 1, 1),
+) -> Optional[List[dt.VideoAnnotation]]:
     frame_annotations = OrderedDict()
     for i in range(volume.shape[view_idx]):
         if view_idx == 2:
@@ -376,3 +485,16 @@ def process_nifti(
         data_array = nib.orientations.apply_orientation(img.get_fdata(), ornt)
         pixdims = img.header.get_zooms()
         return data_array, pixdims
+
+
+def convert_to_dense_rle(raster: np.ndarray) -> List[int]:
+    dense_rle, prev_val, cnt = [], None, 0
+    for val in raster.T.flat:
+        if val == prev_val:
+            cnt += 1
+        else:
+            if prev_val is not None:
+                dense_rle.extend([int(prev_val), int(cnt)])
+            prev_val, cnt = val, 1
+    dense_rle.extend([int(prev_val), int(cnt)])
+    return dense_rle

darwin/importer/formats/nifti_schemas.py

@@ -10,7 +10,7 @@
         "image": {"type": "string"},
         "label": {"type": "string"},
         "class_map": class_map,
-        "mode": {"type": "string", "enum": ["video", "instances"]},
+        "mode": {"type": "string", "enum": ["video", "instances", "mask"]},
         "is_mpr": {"type": "boolean"},
         "slot_names": {"type": "array", "items": {"type": "string"}},
     },

darwin/importer/importer.py

@@ -756,6 +756,8 @@ def _import_annotations(
         if (
             annotation_type not in remote_classes
             or annotation_class.name not in remote_classes[annotation_type]
+            and annotation_type
+            != "raster_layer"  # We do not skip raster layers as they are always available.
         ):
             if annotation_type not in remote_classes:
                 logger.warning(

tests/darwin/importer/formats/import_nifti_test.py

@@ -142,6 +142,65 @@ def test_image_annotation_nifti_import_incorrect_number_slot(team_slug: str):
                 parse_path(path=upload_json)
 
 
+def test_image_annotation_nifti_import_single_slot_to_mask(team_slug: str):
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with ZipFile("tests/data.zip") as zfile:
+            zfile.extractall(tmpdir)
+            label_path = (
+                Path(tmpdir)
+                / team_slug
+                / "nifti"
+                / "releases"
+                / "latest"
+                / "annotations"
+                / "vol0_brain.nii.gz"
+            )
+            input_dict = {
+                "data": [
+                    {
+                        "image": "vol0 (1).nii",
+                        "label": str(label_path),
+                        "class_map": {"1": "brain"},
+                        "mode": "mask",
+                        "is_mpr": False,
+                        "slot_names": ["0.1"],
+                    }
+                ]
+            }
+            upload_json = Path(tmpdir) / "annotations.json"
+            upload_json.write_text(
+                json.dumps(input_dict, indent=4, sort_keys=True, default=str)
+            )
+            annotation_files = parse_path(path=upload_json)
+            annotation_file = annotation_files[0]
+            output_json_string = json.loads(
+                serialise_annotation_file(annotation_file, as_dict=False)
+            )
+            expected_json_string = json.load(
+                open(
+                    Path(tmpdir)
+                    / team_slug
+                    / "nifti"
+                    / "vol0_annotation_file_to_mask.json",
+                    "r",
+                )
+            )
+            # This needs to not check for mask_annotation_ids_mapping as these
+            # are randomly generated
+            [
+                frame.get("raster_layer", {}).pop("mask_annotation_ids_mapping")
+                for frame in output_json_string["annotations"][0]["frames"].values()
+            ]
+            [
+                frame.get("raster_layer", {}).pop("mask_annotation_ids_mapping")
+                for frame in expected_json_string["annotations"][0]["frames"].values()
+            ]
+            assert (
+                output_json_string["annotations"][0]["frames"]
+                == expected_json_string["annotations"][0]["frames"]
+            )
+
+
 def serialise_annotation_file(
     annotation_file: AnnotationFile, as_dict
 ) -> Union[str, dict]: