4258 Add TCIA dataset (#4610)

* Add TCIA dataset

Signed-off-by: Yiheng Wang <[email protected]>

docs/source/apps.rst

@@ -15,6 +15,9 @@ Applications
 .. autoclass:: DecathlonDataset
     :members:
 
+.. autoclass:: TciaDataset
+    :members:
+
 .. autoclass:: CrossValidation
     :members:
 

monai/apps/__init__.py

@@ -9,6 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .datasets import CrossValidation, DecathlonDataset, MedNISTDataset
+from .datasets import CrossValidation, DecathlonDataset, MedNISTDataset, TciaDataset
 from .mmars import MODEL_DESC, RemoteMMARKeys, download_mmar, get_model_spec, load_from_mmar
 from .utils import SUPPORTED_HASH_TYPES, check_hash, download_and_extract, download_url, extractall, get_logger, logger

monai/apps/datasets.py

@@ -9,16 +9,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import shutil
 import sys
+import warnings
 from pathlib import Path
-from typing import Callable, Dict, List, Optional, Sequence, Union
+from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
+from monai.apps.tcia import (
+    download_tcia_series_instance,
+    get_tcia_metadata,
+    get_tcia_ref_uid,
+    match_tcia_ref_uid_in_study,
+)
 from monai.apps.utils import download_and_extract
 from monai.config.type_definitions import PathLike
 from monai.data import (
     CacheDataset,
+    PydicomReader,
     load_decathlon_datalist,
     load_decathlon_properties,
     partition_dataset,
@@ -27,7 +37,7 @@
 from monai.transforms import LoadImaged, Randomizable
 from monai.utils import ensure_tuple
 
-__all__ = ["MedNISTDataset", "DecathlonDataset", "CrossValidation"]
+__all__ = ["MedNISTDataset", "DecathlonDataset", "CrossValidation", "TciaDataset"]
 
 
 class MedNISTDataset(Randomizable, CacheDataset):
@@ -194,8 +204,8 @@ class DecathlonDataset(Randomizable, CacheDataset):
             for further usage, use `AddChanneld` or `AsChannelFirstd` to convert the shape to [C, H, W, D].
         download: whether to download and extract the Decathlon from resource link, default is False.
             if expected file already exists, skip downloading even set it to True.
-        val_frac: percentage of of validation fraction in the whole dataset, default is 0.2.
             user can manually copy tar file or dataset folder to the root directory.
+        val_frac: percentage of of validation fraction in the whole dataset, default is 0.2.
         seed: random seed to randomly shuffle the datalist before splitting into training and validation, default is 0.
             note to set same seed for `training` and `validation` sections.
         cache_num: number of items to be cached. Default is `sys.maxsize`.
@@ -379,6 +389,270 @@ def _split_datalist(self, datalist: List[Dict]) -> List[Dict]:
         return [datalist[i] for i in self.indices]
 
 
+class TciaDataset(Randomizable, CacheDataset):
+    """
+    The Dataset to automatically download the data from a public The Cancer Imaging Archive (TCIA) dataset
+    and generate items for training, validation or test.
+
+    The Highdicom library is used to load dicom data with modality "SEG", but only a part of collections are
+    supoorted, such as: "C4KC-KiTS", "NSCLC-Radiomics", "NSCLC-Radiomics-Interobserver1", " QIN-PROSTATE-Repeatability"
+    and "PROSTATEx". Therefore, if "seg" is included in `keys` of the `LoadImaged` transform and loading some
+    other collections, errors may be raised. For supported collections, the original "SEG" information may not
+    always be consistent for each dicom file. Therefore, to avoid creating different format of labels, please use
+    the `label_dict` argument of `PydicomReader` when calling the `LoadImaged` transform. The prepared label dicts
+    of collections that are mentioned above is also saved in: `monai.apps.tcia.TCIA_LABEL_DICT`. You can also refer
+    to the second example bellow.
+
+
+    This class is based on :py:class:`monai.data.CacheDataset` to accelerate the training process.
+
+    Args:
+        root_dir: user's local directory for caching and loading the TCIA dataset.
+        collection: name of a TCIA collection.
+            a TCIA dataset is defined as a collection. Please check the following list to browse
+            the collection list (only public collections can be downloaded):
+            https://www.cancerimagingarchive.net/collections/
+        section: expected data section, can be: `training`, `validation` or `test`.
+        transform: transforms to execute operations on input data.
+            for further usage, use `AddChanneld` or `AsChannelFirstd` to convert the shape to [C, H, W, D].
+            If not specified, `LoadImaged(reader="PydicomReader", keys=["image"])` will be used as the default
+            transform. In addition, we suggest to set the argument `labels` for `PydicomReader` if segmentations
+            are needed to be loaded. The original labels for each dicom series may be different, using this argument
+            is able to unify the format of labels.
+        download: whether to download and extract the dataset, default is False.
+            if expected file already exists, skip downloading even set it to True.
+            user can manually copy tar file or dataset folder to the root directory.
+        download_len: number of series that will be downloaded, the value should be larger than 0 or -1, where -1 means
+            all series will be downloaded. Default is -1.
+        seg_type: modality type of segmentation that is used to do the first step download. Default is "SEG".
+        modality_tag: tag of modality. Default is (0x0008, 0x0060).
+        ref_series_uid_tag: tag of referenced Series Instance UID. Default is (0x0020, 0x000e).
+        ref_sop_uid_tag: tag of referenced SOP Instance UID. Default is (0x0008, 0x1155).
+        specific_tags: tags that will be loaded for "SEG" series. This argument will be used in
+            `monai.data.PydicomReader`. Default is [(0x0008, 0x1115), (0x0008,0x1140), (0x3006, 0x0010),
+            (0x0020,0x000D), (0x0010,0x0010), (0x0010,0x0020), (0x0020,0x0011), (0x0020,0x0012)].
+        val_frac: percentage of of validation fraction in the whole dataset, default is 0.2.
+        seed: random seed to randomly shuffle the datalist before splitting into training and validation, default is 0.
+            note to set same seed for `training` and `validation` sections.
+        cache_num: number of items to be cached. Default is `sys.maxsize`.
+            will take the minimum of (cache_num, data_length x cache_rate, data_length).
+        cache_rate: percentage of cached data in total, default is 0.0 (no cache).
+            will take the minimum of (cache_num, data_length x cache_rate, data_length).
+        num_workers: the number of worker threads to use.
+            If num_workers is None then the number returned by os.cpu_count() is used.
+            If a value less than 1 is speficied, 1 will be used instead.
+        progress: whether to display a progress bar when downloading dataset and computing the transform cache content.
+        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,
+            default to `True`. if the random transforms don't modify the cached content
+            (for example, randomly crop from the cached image and deepcopy the crop region)
+            or if every cache item is only used once in a `multi-processing` environment,
+            may set `copy=False` for better performance.
+        as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
+            it may help improve the performance of following logic.
+
+    Example::
+
+        # collection is "Pancreatic-CT-CBCT-SEG", seg_type is "RTSTRUCT"
+        data = TciaDataset(
+            root_dir="./", collection="Pancreatic-CT-CBCT-SEG", seg_type="RTSTRUCT", download=True
+        )
+
+        # collection is "C4KC-KiTS", seg_type is "SEG", and load both images and segmentations
+        from monai.apps.tcia import TCIA_LABEL_DICT
+        transform = Compose(
+            [
+                LoadImaged(reader="PydicomReader", keys=["image", "seg"], label_dict=TCIA_LABEL_DICT["C4KC-KiTS"]),
+                EnsureChannelFirstd(keys=["image", "seg"]),
+                ResampleToMatchd(keys="image", key_dst="seg"),
+            ]
+        )
+        data = TciaDataset(
+            root_dir="./", collection="C4KC-KiTS", section="validation", seed=12345, download=True
+        )
+
+        print(data[0]["seg"].shape)
+
+    """
+
+    def __init__(
+        self,
+        root_dir: PathLike,
+        collection: str,
+        section: str,
+        transform: Union[Sequence[Callable], Callable] = (),
+        download: bool = False,
+        download_len: int = -1,
+        seg_type: str = "SEG",
+        modality_tag: Tuple = (0x0008, 0x0060),
+        ref_series_uid_tag: Tuple = (0x0020, 0x000E),
+        ref_sop_uid_tag: Tuple = (0x0008, 0x1155),
+        specific_tags: Tuple = (
+            (0x0008, 0x1115),  # Referenced Series Sequence
+            (0x0008, 0x1140),  # Referenced Image Sequence
+            (0x3006, 0x0010),  # Referenced Frame of Reference Sequence
+            (0x0020, 0x000D),  # Study Instance UID
+            (0x0010, 0x0010),  # Patient's Name
+            (0x0010, 0x0020),  # Patient ID
+            (0x0020, 0x0011),  # Series Number
+            (0x0020, 0x0012),  # Acquisition Number
+        ),
+        seed: int = 0,
+        val_frac: float = 0.2,
+        cache_num: int = sys.maxsize,
+        cache_rate: float = 0.0,
+        num_workers: int = 1,
+        progress: bool = True,
+        copy_cache: bool = True,
+        as_contiguous: bool = True,
+    ) -> None:
+        root_dir = Path(root_dir)
+        if not root_dir.is_dir():
+            raise ValueError("Root directory root_dir must be a directory.")
+
+        self.section = section
+        self.val_frac = val_frac
+        self.seg_type = seg_type
+        self.modality_tag = modality_tag
+        self.ref_series_uid_tag = ref_series_uid_tag
+        self.ref_sop_uid_tag = ref_sop_uid_tag
+
+        self.set_random_state(seed=seed)
+        download_dir = os.path.join(root_dir, collection)
+        load_tags = list(specific_tags)
+        load_tags += [modality_tag]
+        self.load_tags = load_tags
+        if download:
+            seg_series_list = get_tcia_metadata(
+                query=f"getSeries?Collection={collection}&Modality={seg_type}", attribute="SeriesInstanceUID"
+            )
+            if download_len > 0:
+                seg_series_list = seg_series_list[:download_len]
+            if len(seg_series_list) == 0:
+                raise ValueError(f"Cannot find data with collection: {collection} seg_type: {seg_type}")
+            for series_uid in seg_series_list:
+                self._download_series_reference_data(series_uid, download_dir)
+
+        if not os.path.exists(download_dir):
+            raise RuntimeError(f"Cannot find dataset directory: {download_dir}.")
+
+        self.indices: np.ndarray = np.array([])
+        self.datalist = self._generate_data_list(download_dir)
+
+        if transform == ():
+            transform = LoadImaged(reader="PydicomReader", keys=["image"])
+        CacheDataset.__init__(
+            self,
+            data=self.datalist,
+            transform=transform,
+            cache_num=cache_num,
+            cache_rate=cache_rate,
+            num_workers=num_workers,
+            progress=progress,
+            copy_cache=copy_cache,
+            as_contiguous=as_contiguous,
+        )
+
+    def get_indices(self) -> np.ndarray:
+        """
+        Get the indices of datalist used in this dataset.
+
+        """
+        return self.indices
+
+    def randomize(self, data: np.ndarray) -> None:
+        self.R.shuffle(data)
+
+    def _download_series_reference_data(self, series_uid: str, download_dir: str):
+        """
+        First of all, download a series from TCIA according to `series_uid`.
+        Then find all referenced series and download.
+        """
+        seg_first_dir = os.path.join(download_dir, "raw", series_uid)
+        download_tcia_series_instance(
+            series_uid=series_uid, download_dir=download_dir, output_dir=seg_first_dir, check_md5=False
+        )
+        dicom_files = [f for f in os.listdir(seg_first_dir) if f.endswith(".dcm")]
+        # achieve series number and patient id from the first dicom file
+        dcm_path = os.path.join(seg_first_dir, dicom_files[0])
+        ds = PydicomReader(stop_before_pixels=True, specific_tags=self.load_tags).read(dcm_path)
+        # (0x0010,0x0020) and (0x0010,0x0010), better to be contained in `specific_tags`
+        patient_id = ds.PatientID if ds.PatientID else ds.PatientName
+        if not patient_id:
+            warnings.warn(f"unable to find patient name of dicom file: {dcm_path}, use 'patient' instead.")
+            patient_id = "patient"
+        # (0x0020,0x0011) and (0x0020,0x0012), better to be contained in `specific_tags`
+        series_num = ds.SeriesNumber if ds.SeriesNumber else ds.AcquisitionNumber
+        if not series_num:
+            warnings.warn(f"unable to find series number of dicom file: {dcm_path}, use '0' instead.")
+            series_num = 0
+
+        series_num = str(series_num)
+        seg_dir = os.path.join(download_dir, patient_id, series_num, self.seg_type.lower())
+        dcm_dir = os.path.join(download_dir, patient_id, series_num, "image")
+
+        # get ref uuid
+        ref_uid_list = []
+        for dcm_file in dicom_files:
+            dcm_path = os.path.join(seg_first_dir, dcm_file)
+            ds = PydicomReader(stop_before_pixels=True, specific_tags=self.load_tags).read(dcm_path)
+            if ds[self.modality_tag].value == self.seg_type:
+                ref_uid = get_tcia_ref_uid(
+                    ds, find_sop=False, ref_series_uid_tag=self.ref_series_uid_tag, ref_sop_uid_tag=self.ref_sop_uid_tag
+                )
+                if ref_uid == "":
+                    ref_sop_uid = get_tcia_ref_uid(
+                        ds,
+                        find_sop=True,
+                        ref_series_uid_tag=self.ref_series_uid_tag,
+                        ref_sop_uid_tag=self.ref_sop_uid_tag,
+                    )
+                    ref_uid = match_tcia_ref_uid_in_study(ds.StudyInstanceUID, ref_sop_uid)
+                if ref_uid != "":
+                    ref_uid_list.append(ref_uid)
+        if not ref_uid_list:
+            warnings.warn(f"Cannot find the referenced Series Instance UID from series: {series_uid}.")
+        else:
+            download_tcia_series_instance(
+                series_uid=ref_uid_list[0], download_dir=download_dir, output_dir=dcm_dir, check_md5=False
+            )
+        if not os.path.exists(seg_dir):
+            shutil.copytree(seg_first_dir, seg_dir)
+
+    def _generate_data_list(self, dataset_dir: PathLike) -> List[Dict]:
+        # the types of the item in data list should be compatible with the dataloader
+        dataset_dir = Path(dataset_dir)
+        datalist = []
+        patient_list = [f.name for f in os.scandir(dataset_dir) if f.is_dir() and f.name != "raw"]
+        for patient_id in patient_list:
+            series_list = [f.name for f in os.scandir(os.path.join(dataset_dir, patient_id)) if f.is_dir()]
+            for series_num in series_list:
+                seg_key = self.seg_type.lower()
+                image_path = os.path.join(dataset_dir, patient_id, series_num, "image")
+                mask_path = os.path.join(dataset_dir, patient_id, series_num, seg_key)
+
+                if os.path.exists(image_path):
+                    datalist.append({"image": image_path, seg_key: mask_path})
+                else:
+                    datalist.append({seg_key: mask_path})
+
+        return self._split_datalist(datalist)
+
+    def _split_datalist(self, datalist: List[Dict]) -> List[Dict]:
+        if self.section == "test":
+            return datalist
+        length = len(datalist)
+        indices = np.arange(length)
+        self.randomize(indices)
+
+        val_length = int(length * self.val_frac)
+        if self.section == "training":
+            self.indices = indices[val_length:]
+        else:
+            self.indices = indices[:val_length]
+
+        return [datalist[i] for i in self.indices]
+
+
 class CrossValidation:
     """
     Cross validation dataset based on the general dataset which must have `_split_datalist` API.

monai/apps/tcia/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 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.
+
+from .label_desc import TCIA_LABEL_DICT
+from .utils import download_tcia_series_instance, get_tcia_metadata, get_tcia_ref_uid, match_tcia_ref_uid_in_study

monai/apps/tcia/label_desc.py

@@ -0,0 +1,49 @@
+# Copyright (c) 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.
+
+
+from typing import Dict
+
+__all__ = ["TCIA_LABEL_DICT"]
+
+
+TCIA_LABEL_DICT: Dict[str, Dict[str, int]] = {
+    "C4KC-KiTS": {"Kidney": 0, "Renal Tumor": 1},
+    "NSCLC-Radiomics": {
+        "Esophagus": 0,
+        "GTV-1": 1,
+        "Lungs-Total": 2,
+        "Spinal-Cord": 3,
+        "Lung-Left": 4,
+        "Lung-Right": 5,
+        "Heart": 6,
+    },
+    "NSCLC-Radiomics-Interobserver1": {
+        "GTV-1auto-1": 0,
+        "GTV-1auto-2": 1,
+        "GTV-1auto-3": 2,
+        "GTV-1auto-4": 3,
+        "GTV-1auto-5": 4,
+        "GTV-1vis-1": 5,
+        "GTV-1vis-2": 6,
+        "GTV-1vis-3": 7,
+        "GTV-1vis-4": 8,
+        "GTV-1vis-5": 9,
+    },
+    "QIN-PROSTATE-Repeatability": {"NormalROI_PZ_1": 0, "TumorROI_PZ_1": 1, "PeripheralZone": 2, "WholeGland": 3},
+    "PROSTATEx": {
+        "Prostate": 0,
+        "Peripheral zone of prostate": 1,
+        "Transition zone of prostate": 2,
+        "Distal prostatic urethra": 3,
+        "Anterior fibromuscular stroma of prostate": 4,
+    },
+}