[WIP] Support to train/run Deepgrow 2D/3D models

docs/source/apps.rst

@@ -28,3 +28,35 @@ Applications
 .. autofunction:: extractall
 
 .. autofunction:: download_and_extract
+
+`Deepgrow`
+----------
+
+.. automodule:: monai.apps.deepgrow.dataset
+.. autofunction:: create_dataset
+
+.. automodule:: monai.apps.deepgrow.interaction
+.. autoclass:: Interaction
+    :members:
+
+.. automodule:: monai.apps.deepgrow.transforms
+.. autoclass:: AddInitialSeedPointd
+    :members:
+.. autoclass:: AddGuidanceSignald
+    :members:
+.. autoclass:: AddRandomGuidanced
+    :members:
+.. autoclass:: AddGuidanceFromPointsd
+    :members:
+.. autoclass:: SpatialCropForegroundd
+    :members:
+.. autoclass:: SpatialCropGuidanced
+    :members:
+.. autoclass:: RestoreCroppedLabeld
+    :members:
+.. autoclass:: FindDiscrepancyRegionsd
+    :members:
+.. autoclass:: FindAllValidSlicesd
+    :members:
+.. autoclass:: Fetch2DSliced
+    :members:

monai/apps/deepgrow/__init__.py

@@ -0,0 +1,10 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

monai/apps/deepgrow/dataset.py

@@ -0,0 +1,268 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+from typing import Dict, List
+
+import numpy as np
+
+from monai.transforms import AsChannelFirstd, Compose, LoadImaged, Orientationd, Spacingd
+from monai.utils import GridSampleMode
+
+
+def create_dataset(
+    datalist,
+    output_dir,
+    dimension,
+    pixdim,
+    keys=("image", "label"),
+    base_dir=None,
+    limit=0,
+    relative_path=False,
+    transforms=None,
+) -> List[Dict]:
+    """
+    Utility to pre-process and create dataset list for Deepgrow training over on existing one.
+    The input data list is normally a list of images and labels (3D volume) that needs pre-processing
+    for Deepgrow training pipeline.
+
+    Args:
+        datalist: A generic dataset with a length property which normally contains a list of data dictionary.
+            For example, typical input data can be a list of dictionaries::
+
+                [{'image': 'img1.nii', 'label': 'label1.nii'}]
+
+        output_dir: target directory to store the training data for Deepgrow Training
+        pixdim: output voxel spacing.
+        dimension: dimension for Deepgrow training.  It can be 2 or 3.
+        keys: Image and Label keys in input datalist.  Defaults to 'image' and 'label'
+        base_dir: base directory in case related path is used for the keys in datalist.  Defaults to None.
+        limit: limit number of inputs for pre-processing.  Defaults to 0 (no limit).
+        relative_path: output keys values should be based on relative path.  Defaults to False.
+        transforms: explicit transforms to execute operations on input data.
+
+    Raises:
+        ValueError: When ``dimension`` is not one of [2, 3]
+        ValueError: When ``datalist`` is Empty
+
+    Example::
+
+        datalist = create_dataset(
+            datalist=[{'image': 'img1.nii', 'label': 'label1.nii'}],
+            base_dir=None,
+            output_dir=output_2d,
+            dimension=2,
+            keys=('image', 'label')
+            pixdim=(1.0, 1.0),
+            limit=0,
+            relative_path=True
+        )
+
+        print(datalist[0]["image"], datalist[0]["label"])
+    """
+
+    if dimension not in [2, 3]:
+        raise ValueError("Dimension can be only 2 or 3 as Deepgrow supports only 2D/3D Training")
+
+    if not len(datalist):
+        raise ValueError("Input Datalist is empty")
+
+    if not isinstance(keys, list) and not isinstance(keys, tuple):
+        keys = [keys]
+
+    transforms = _default_transforms(keys, pixdim) if transforms is None else transforms
+    new_datalist = []
+    for idx in range(len(datalist)):
+        if limit and idx >= limit:
+            break
+
+        image = datalist[idx][keys[0]]
+        label = datalist[idx].get(keys[1]) if len(keys) > 1 else None
+        if base_dir:
+            image = os.path.join(base_dir, image)
+            label = os.path.join(base_dir, label) if label else None
+
+        image = os.path.abspath(image)
+        label = os.path.abspath(label) if label else None
+
+        logging.info("Image: {}; Label: {}".format(image, label if label else None))
+        if dimension == 2:
+            data = _save_data_2d(
+                vol_idx=idx,
+                data=transforms({"image": image, "label": label}),
+                keys=("image", "label"),
+                dataset_dir=output_dir,
+                relative_path=relative_path,
+            )
+        else:
+            data = _save_data_3d(
+                vol_idx=idx,
+                data=transforms({"image": image, "label": label}),
+                keys=("image", "label"),
+                dataset_dir=output_dir,
+                relative_path=relative_path,
+            )
+        new_datalist.extend(data)
+    return new_datalist
+
+
+def _default_transforms(keys, pixdim):
+    mode = [GridSampleMode.BILINEAR, GridSampleMode.NEAREST] if len(keys) == 2 else [GridSampleMode.BILINEAR]
+    return Compose([
+        LoadImaged(keys=keys),
+        AsChannelFirstd(keys=keys),
+        Spacingd(keys=keys, pixdim=pixdim, mode=mode),
+        Orientationd(keys=keys, axcodes="RAS"),
+    ])
+
+
+def _save_data_2d(vol_idx, data, keys, dataset_dir, relative_path):
+    vol_image = data[keys[0]]
+    vol_label = data.get(keys[1])
+    data_list = []
+
+    if len(vol_image.shape) == 4:
+        logging.info(
+            "4D-Image, pick only first series; Image: {}; Label: {}".format(
+                vol_image.shape, vol_label.shape if vol_label else None
+            )
+        )
+        vol_image = vol_image[0]
+        vol_image = np.moveaxis(vol_image, -1, 0)
+
+    image_count = 0
+    label_count = 0
+    unique_labels_count = 0
+    for sid in range(vol_image.shape[0]):
+        image = vol_image[sid, ...]
+        label = vol_label[sid, ...] if vol_label is not None else None
+
+        if vol_label is not None and np.sum(label) == 0:
+            continue
+
+        image_file_prefix = "vol_idx_{:0>4d}_slice_{:0>3d}".format(vol_idx, sid)
+        image_file = os.path.join(dataset_dir, "images", image_file_prefix)
+        image_file += ".npy"
+
+        os.makedirs(os.path.join(dataset_dir, "images"), exist_ok=True)
+        np.save(image_file, image)
+        image_count += 1
+
+        # Test Data
+        if vol_label is None:
+            data_list.append(
+                {
+                    "image": image_file.replace(dataset_dir + "/", "") if relative_path else image_file,
+                }
+            )
+            continue
+
+        # For all Labels
+        unique_labels = np.unique(label.flatten())
+        unique_labels = unique_labels[unique_labels != 0]
+        unique_labels_count = max(unique_labels_count, len(unique_labels))
+
+        for idx in unique_labels:
+            label_file_prefix = "{}_region_{:0>2d}".format(image_file_prefix, int(idx))
+            label_file = os.path.join(dataset_dir, "labels", label_file_prefix)
+            label_file += ".npy"
+
+            os.makedirs(os.path.join(dataset_dir, "labels"), exist_ok=True)
+            curr_label = (label == idx).astype(np.float32)
+            np.save(label_file, curr_label)
+
+            label_count += 1
+            data_list.append(
+                {
+                    "image": image_file.replace(dataset_dir + "/", "") if relative_path else image_file,
+                    "label": label_file.replace(dataset_dir + "/", "") if relative_path else label_file,
+                    "region": int(idx),
+                }
+            )
+
+    logging.info(
+        "{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}".format(
+            vol_idx,
+            vol_image.shape,
+            image_count,
+            vol_label.shape if vol_label is not None else None,
+            label_count,
+            unique_labels_count,
+        )
+    )
+    return data_list
+
+
+def _save_data_3d(vol_idx, data, keys, dataset_dir, relative_path):
+    vol_image = data[keys[0]]
+    vol_label = data.get(keys[1])
+    data_list = []
+
+    if len(vol_image.shape) == 4:
+        logging.info("4D-Image, pick only first series; Image: {}; Label: {}".format(vol_image.shape, vol_label.shape))
+        vol_image = vol_image[0]
+        vol_image = np.moveaxis(vol_image, -1, 0)
+
+    image_count = 0
+    label_count = 0
+    unique_labels_count = 0
+
+    image_file_prefix = "vol_idx_{:0>4d}".format(vol_idx)
+    image_file = os.path.join(dataset_dir, "images", image_file_prefix)
+    image_file += ".npy"
+
+    os.makedirs(os.path.join(dataset_dir, "images"), exist_ok=True)
+    np.save(image_file, vol_image)
+    image_count += 1
+
+    # Test Data
+    if vol_label is None:
+        data_list.append(
+            {
+                "image": image_file.replace(dataset_dir + "/", "") if relative_path else image_file,
+            }
+        )
+    else:
+        # For all Labels
+        unique_labels = np.unique(vol_label.flatten())
+        unique_labels = unique_labels[unique_labels != 0]
+        unique_labels_count = max(unique_labels_count, len(unique_labels))
+
+        for idx in unique_labels:
+            label_file_prefix = "{}_region_{:0>2d}".format(image_file_prefix, int(idx))
+            label_file = os.path.join(dataset_dir, "labels", label_file_prefix)
+            label_file += ".npy"
+
+            curr_label = (vol_label == idx).astype(np.float32)
+            os.makedirs(os.path.join(dataset_dir, "labels"), exist_ok=True)
+            np.save(label_file, curr_label)
+
+            label_count += 1
+            data_list.append(
+                {
+                    "image": image_file.replace(dataset_dir + "/", "") if relative_path else image_file,
+                    "label": label_file.replace(dataset_dir + "/", "") if relative_path else label_file,
+                    "region": int(idx),
+                }
+            )
+
+    logging.info(
+        "{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}".format(
+            vol_idx,
+            vol_image.shape,
+            image_count,
+            vol_label.shape if vol_label is not None else None,
+            label_count,
+            unique_labels_count,
+        )
+    )
+    return data_list

monai/apps/deepgrow/interaction.py

@@ -0,0 +1,81 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import importlib
+from typing import Dict
+
+import torch
+
+from monai.engines.utils import CommonKeys
+from monai.engines.workflow import Engine, Events
+from monai.transforms import Compose
+
+
+class Interaction:
+    """
+    Ignite handler used to introduce interactions (simulation of clicks) for Deepgrow Training/Evaluation.
+
+    Args:
+        transforms: execute additional transformation during every iteration (before train).
+            Typically, several Tensor based transforms composed by `Compose`.
+        max_interactions: maximum number of interactions per iteration
+        train: training or evaluation
+        key_probability: field name to fill probability for every interaction
+    """
+
+    def __init__(self, transforms, max_interactions: int, train: bool, key_probability: str = "probability") -> None:
+        self.transforms = transforms
+        self.max_interactions = max_interactions
+        self.train = train
+        self.key_probability = key_probability
+
+        if not isinstance(self.transforms, Compose):
+            transforms = []
+            for t in self.transforms:
+                transforms.append(self.init_external_class(t))
+            self.transforms = Compose(transforms)
+
+    @staticmethod
+    def init_external_class(config_dict):
+        class_args = None if config_dict.get("args") is None else dict(config_dict.get("args"))
+        class_path = config_dict.get("path", config_dict["name"])
+
+        module_name, class_name = class_path.rsplit(".", 1)
+        m = importlib.import_module(module_name)
+        c = getattr(m, class_name)
+        return c(**class_args) if class_args else c()
+
+    def attach(self, engine: Engine) -> None:
+        if not engine.has_event_handler(self, Events.ITERATION_STARTED):
+            engine.add_event_handler(Events.ITERATION_STARTED, self)
+
+    def __call__(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):
+        if batchdata is None:
+            raise ValueError("Must provide batch data for current iteration.")
+
+        for j in range(self.max_interactions):
+            inputs, _ = engine.prepare_batch(batchdata)
+            inputs = inputs.to(engine.state.device)
+
+            engine.network.eval()
+            with torch.no_grad():
+                if engine.amp:
+                    with torch.cuda.amp.autocast():
+                        predictions = engine.inferer(inputs, engine.network)
+                else:
+                    predictions = engine.inferer(inputs, engine.network)
+
+            batchdata.update({CommonKeys.PRED: predictions})
+            batchdata[self.key_probability] = torch.as_tensor(
+                ([1.0 - ((1.0 / self.max_interactions) * j)] if self.train else [1.0]) * len(inputs)
+            )
+            batchdata = self.transforms(batchdata)
+
+        return engine._iteration(engine, batchdata)