Source code for monai.deploy.operators.dicom_seg_writer_operator

# Copyright 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import datetime
import logging
import os
from pathlib import Path
from random import randint
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Union

import numpy as np
from typeguard import typechecked

from monai.deploy.utils.importutil import optional_import
from monai.deploy.utils.version import get_sdk_semver

dcmread, _ = optional_import("pydicom", name="dcmread")
generate_uid, _ = optional_import("pydicom.uid", name="generate_uid")
ImplicitVRLittleEndian, _ = optional_import("pydicom.uid", name="ImplicitVRLittleEndian")
Dataset, _ = optional_import("pydicom.dataset", name="Dataset")
FileDataset, _ = optional_import("pydicom.dataset", name="FileDataset")
sitk, _ = optional_import("SimpleITK")
codes, _ = optional_import("", name="codes")
    import highdicom as hd
    from import Code
    Code, _ = optional_import("", name="Code")
    hd, _ = optional_import("highdicom")

import monai.deploy.core as md
from monai.deploy.core import DataPath, ExecutionContext, Image, InputContext, IOType, Operator, OutputContext
from monai.deploy.core.domain.dicom_series import DICOMSeries
from monai.deploy.core.domain.dicom_series_selection import StudySelectedSeries

class SegmentDescription:
    def __init__(
        segment_label: str,
        segmented_property_category: Code,
        segmented_property_type: Code,
        algorithm_name: str,
        algorithm_version: str,
        algorithm_family: Code = codes.DCM.ArtificialIntelligence,
        tracking_id: Optional[str] = None,
        tracking_uid: Optional[str] = None,
        anatomic_regions: Optional[Sequence[Code]] = None,
        primary_anatomic_structures: Optional[Sequence[Code]] = None,
        """Class encapsulating the description of a segment within the segmentation.

        segment_label: str
            User-defined label identifying this segment,
            DICOM VR Long String (LO) (see C.8.20-4
            "Segment Description Macro Attributes")
            Category of the property the segment represents,
            e.g. ``Code("49755003", "SCT", "Morphologically Abnormal
            Structure")`` (see CID 7150
            "Segmentation Property Categories")
            Property the segment represents,
            e.g. ``Code("108369006", "SCT", "Neoplasm")`` (see CID 7151
            "Segmentation Property Types")
        algorithm_name: str
            Name of algorithm used to generate the segment, also as the name assigned by a
            manufacturer to a specific software algorithm,
            DICOM VR Long String (LO) (see C.8.20-2
            "Segmentation Image Module Attribute", and see 10-19
            "Algorithm Identification Macro Attributes")
        algorithm_version: str
            The software version identifier assigned by a manufacturer to a specific software algorithm,
            DICOM VR Long String (LO) (see 10-19
            "Algorithm Identification Macro Attributes")
        tracking_id: Optional[str], optional
            Tracking identifier (unique only with the domain of use).
        tracking_uid: Optional[str], optional
            Unique tracking identifier (universally unique) in the DICOM format
            for UIDs. This is only permissible if a ``tracking_id`` is also
            supplied. You may use ``pydicom.uid.generate_uid`` to generate a
            suitable UID. If ``tracking_id`` is supplied but ``tracking_uid`` is
            not supplied, a suitable UID will be generated for you.
        anatomic_regions: Optional[Sequence[]], optional
            Anatomic region(s) into which segment falls,
            e.g. ``Code("41216001", "SCT", "Prostate")`` (see CID 4
            "Anatomic Region", CID 403
            "Common Anatomic Regions", as as well as other CIDs for
            domain-specific anatomic regions)
        primary_anatomic_structures: Optional[Sequence[]], optional
            Anatomic structure(s) the segment represents
            (see CIDs for domain-specific primary anatomic structures)
        self._segment_label = segment_label
        self._segmented_property_category = segmented_property_category
        self._segmented_property_type = segmented_property_type
        self._tracking_id = tracking_id

        self._anatomic_regions = anatomic_regions
        self._primary_anatomic_structures = primary_anatomic_structures

        # Generate a UID if one was not provided
        if tracking_id is not None and tracking_uid is None:
            tracking_uid = hd.UID()
        self._tracking_uid = tracking_uid

        self._algorithm_identification = hd.AlgorithmIdentificationSequence(

    def to_segment_description(self, segment_number: int) -> hd.seg.SegmentDescription:
        """Get a corresponding highdicom Segment Description object.

        segment_number: int
            Number of the segment. Must start at 1 and increase by 1 within a
            given segmentation object.

            highdicom Segment Description containing the information in this
        return hd.seg.SegmentDescription(

[docs]@md.input("seg_image", Image, IOType.IN_MEMORY) @md.input("study_selected_series_list", List[StudySelectedSeries], IOType.IN_MEMORY) @md.output("dicom_seg_instance", DataPath, IOType.DISK) @md.env(pip_packages=["pydicom >= 2.3.0", "highdicom >= 0.18.2"]) class DICOMSegmentationWriterOperator(Operator): """ This operator writes out a DICOM Segmentation Part 10 file to disk """ # Supported input image format, based on extension. SUPPORTED_EXTENSIONS = [".nii", ".nii.gz", ".mhd"] # DICOM instance file extension. Case insensitive in string comparison. DCM_EXTENSION = ".dcm" def __init__( self, segment_descriptions: List[SegmentDescription], custom_tags: Optional[Dict[str, str]] = None, omit_empty_frames: bool = True, *args, **kwargs, ): super().__init__(*args, **kwargs) """Instantiates the DICOM Seg Writer instance with optional list of segment label strings. Each unique, non-zero integer value in the segmentation image represents a segment that must be described by an item of the segment descriptions list with the corresponding segment number. Items in the list must be arranged starting at segment number 1 and increasing by 1. For example, in the CT Spleen Segmentation application, the whole image background has a value of 0, and the Spleen segment of value 1. This then only requires the caller to pass in a list containing a segment description, which is used as label for the Spleen in the DICOM Seg instance. Note: this interface is subject to change. It is planned that a new object will encapsulate the segment label information, including label value, name, description etc. Args: segment_descriptions: List[SegmentDescription] Object encapsulating the description of each segment present in the segmentation. custom_tags: Optional[Dict[str, str]], optional Dictionary for setting custom DICOM tags using Keywords and str values only omit_empty_frames: bool, optional Whether to omit frames that contain no segmented pixels from the output segmentation. """ self._seg_descs = [sd.to_segment_description(n) for n, sd in enumerate(segment_descriptions, 1)] self._custom_tags = custom_tags self._omit_empty_frames = omit_empty_frames
[docs] def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext): """Performs computation for this operator and handles I/O. For now, only a single segmentation image object or file is supported and the selected DICOM series for inference is required, because the DICOM Seg IOD needs to refer to original instance. When there are multiple selected series in the input, the first series' containing study will be used for retrieving DICOM Study module attributes, e.g. StudyInstanceUID. Raises: FileNotFoundError: When image object not in the input, and segmentation image file not found either. ValueError: Neither image object nor image file's folder is in the input, or no selected series. """ # Gets the input, prepares the output folder, and then delegates the processing. study_selected_series_list = op_input.get("study_selected_series_list") if not study_selected_series_list or len(study_selected_series_list) < 1: raise ValueError("Missing input, list of 'StudySelectedSeries'.") for study_selected_series in study_selected_series_list: if not isinstance(study_selected_series, StudySelectedSeries): raise ValueError("Element in input is not expected type, 'StudySelectedSeries'.") seg_image = op_input.get("seg_image") # In case the Image object is not in the input, and input is the seg image file folder path. if not isinstance(seg_image, Image): if isinstance(seg_image, DataPath): seg_image, _ = self.select_input_file(seg_image.path) else: raise ValueError("Input 'seg_image' is not Image or DataPath.") output_dir = op_output.get().path output_dir.mkdir(parents=True, exist_ok=True) self.process_images(seg_image, study_selected_series_list, output_dir)
def process_images( self, image: Union[Image, Path], study_selected_series_list: List[StudySelectedSeries], output_dir: Path ): """ """ # Get the seg image in numpy, and if the image is passed in as object, need to fake a input path. seg_image_numpy = None input_path = "dicom_seg" if isinstance(image, Image): seg_image_numpy = image.asnumpy() elif isinstance(image, Path): input_path = str(image) # It is expected that this is the image file path. seg_image_numpy = self._image_file_to_numpy(input_path) else: raise ValueError("'image' is not an Image object or a supported image file.") # Pick DICOM Series that was used as input for getting the seg image. # For now, first one in the list. for study_selected_series in study_selected_series_list: if not isinstance(study_selected_series, StudySelectedSeries): raise ValueError("Element in input is not expected type, 'StudySelectedSeries'.") selected_series = study_selected_series.selected_series[0] dicom_series = selected_series.series self.create_dicom_seg(seg_image_numpy, dicom_series, output_dir) break def create_dicom_seg(self, image: np.ndarray, dicom_series: DICOMSeries, output_dir: Path): # Generate SOP instance UID, and use it as dcm file name too seg_sop_instance_uid = hd.UID() # generate_uid() can be used too. if not output_dir.is_dir(): try: output_dir.mkdir(parents=True, exist_ok=True) except Exception: raise ValueError("output_dir {output_dir} does not exist and failed to be created.") from None output_path = output_dir / f"{seg_sop_instance_uid}{DICOMSegmentationWriterOperator.DCM_EXTENSION}" dicom_dataset_list = [i.get_native_sop_instance() for i in dicom_series.get_sop_instances()] try: version_str = get_sdk_semver() # SDK Version except Exception: version_str = "0.1" # Fall back to the initial version seg = hd.seg.Segmentation( source_images=dicom_dataset_list, pixel_array=image, segmentation_type=hd.seg.SegmentationTypeValues.BINARY, segment_descriptions=self._seg_descs, series_instance_uid=hd.UID(), series_number=random_with_n_digits(4), sop_instance_uid=seg_sop_instance_uid, instance_number=1, manufacturer="The MONAI Consortium", manufacturer_model_name="MONAI Deploy App SDK", software_versions=version_str, device_serial_number="0000", omit_empty_frames=self._omit_empty_frames, ) # Adding a few tags that are not in the Dataset # Also try to set the custom tags that are of string type dt_now = seg.SeriesDate = dt_now.strftime("%Y%m%d") seg.SeriesTime = dt_now.strftime("%H%M%S") seg.TimezoneOffsetFromUTC = ( dt_now.astimezone().isoformat()[-6:].replace(":", "") ) # '2022-09-27T22:36:20.143857-07:00' if self._custom_tags: for k, v in self._custom_tags.items(): if isinstance(k, str) and isinstance(v, str): try: if k in seg: data_element = seg.data_element(k) if data_element: data_element.value = v else: seg.update({k: v}) # type: ignore except Exception as ex: # Best effort for now. logging.warning(f"Tag {k} was not written, due to {ex}") seg.save_as(output_path) try: # Test reading back _ = self._read_from_dcm(str(output_path)) except Exception as ex: print("DICOMSeg creation failed. Error:\n{}".format(ex)) raise def _read_from_dcm(self, file_path: str): """Read dcm file into pydicom Dataset Args: file_path (str): The path to dcm file """ return dcmread(file_path)
[docs] def select_input_file(self, input_folder, extensions=SUPPORTED_EXTENSIONS): """Select the input files based on supported extensions. Args: input_folder (string): the path of the folder containing the input file(s) extensions (array): the supported file formats identified by the extensions. Returns: file_path (string) : The path of the selected file ext (string): The extension of the selected file """ def which_supported_ext(file_path, extensions): for ext in extensions: if file_path.casefold().endswith(ext.casefold()): return ext return None if os.path.isdir(input_folder): for file_name in os.listdir(input_folder): file_path = os.path.join(input_folder, file_name) if os.path.isfile(file_path): ext = which_supported_ext(file_path, extensions) if ext: return file_path, ext raise IOError("No supported input file found ({})".format(extensions)) elif os.path.isfile(input_folder): ext = which_supported_ext(input_folder, extensions) if ext: return input_folder, ext else: raise FileNotFoundError("{} is not found.".format(input_folder))
def _image_file_to_numpy(self, input_path: str): """Converts image file to numpy""" img = sitk.ReadImage(input_path) data_np = sitk.GetArrayFromImage(img) if data_np is None: raise RuntimeError("Failed to convert image file to numpy: {}".format(input_path)) return data_np.astype(np.uint8)
def random_with_n_digits(n): assert isinstance(n, int), "Argument n must be a int." n = n if n >= 1 else 1 range_start = 10 ** (n - 1) range_end = (10**n) - 1 return randint(range_start, range_end) def test(): from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator current_file_dir = Path(__file__).parent.resolve() data_path = current_file_dir.joinpath("../../../inputs/spleen_ct_tcia") out_dir = Path("output_seg_op").absolute() segment_descriptions = [ SegmentDescription( segment_label="Spleen", segmented_property_category=codes.SCT.Organ, segmented_property_type=codes.SCT.Spleen, algorithm_name="Test algorithm", algorithm_family=codes.DCM.ArtificialIntelligence, algorithm_version="0.0.2", ) ] loader = DICOMDataLoaderOperator() series_selector = DICOMSeriesSelectorOperator() dcm_to_volume_op = DICOMSeriesToVolumeOperator() seg_writer = DICOMSegmentationWriterOperator(segment_descriptions) # Testing with more granular functions study_list = loader.load_data_to_studies(data_path.absolute()) series = study_list[0].get_all_series()[0] dcm_to_volume_op.prepare_series(series) voxels = dcm_to_volume_op.generate_voxel_data(series) metadata = dcm_to_volume_op.create_metadata(series) image = dcm_to_volume_op.create_volumetric_image(voxels, metadata) # Very crude thresholding image_numpy = (image.asnumpy() > 400).astype(np.uint8) seg_writer.create_dicom_seg(image_numpy, series, out_dir) # Testing with the main entry functions study_list = loader.load_data_to_studies(data_path.absolute()) study_selected_series_list = series_selector.filter(None, study_list) image = dcm_to_volume_op.convert_to_image(study_selected_series_list) # Very crude thresholding image_numpy = (image.asnumpy() > 400).astype(np.uint8) image = Image(image_numpy) seg_writer.process_images(image, study_selected_series_list, out_dir) if __name__ == "__main__": test()