Source code for monai.deploy.operators.dicom_series_to_volume_operator

# Copyright 2021-2022 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 copy
import logging
import math
from typing import Dict, List, Union

import numpy as np

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

[docs]@md.input("study_selected_series_list", List[StudySelectedSeries], IOType.IN_MEMORY) @md.output("image", Image, IOType.IN_MEMORY) class DICOMSeriesToVolumeOperator(Operator): """This operator converts an instance of DICOMSeries into an Image object. The loaded Image Object can be used for further processing via other operators. The data array will be a 3D image NumPy array with index order of `DHW`. Channel is limited to 1 as of now, and `C` is absent in the NumPy array. """
[docs] def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext): """Performs computation for this operator and handles I/O.""" study_selected_series_list = op_input.get("study_selected_series_list") # TODO: need to get a solution to correctly annotate and consume multiple image outputs. # For now, only supports the one and only one selected series. image = self.convert_to_image(study_selected_series_list) op_output.set(image, "image")
[docs] def convert_to_image(self, study_selected_series_list: List[StudySelectedSeries]) -> Union[Image, None]: """Extracts the pixel data from a DICOM Series and other attributes to create an Image object""" # For now, only supports the one and only one selected series. if not study_selected_series_list or len(study_selected_series_list) < 1: raise ValueError("Missing expected input 'study_selected_series_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 selection_name = selected_series.selection_name self.prepare_series(dicom_series) metadata = self.create_metadata(dicom_series) # Add to the metadata the DICOMStudy properties and selection metadata metadata.update(self._get_instance_properties( selection_metadata = {"selection_name": selection_name} metadata.update(selection_metadata) voxel_data = self.generate_voxel_data(dicom_series) image = self.create_volumetric_image(voxel_data, metadata) # Now it is time to assign the converted image to the SelectedSeries obj selected_series.image = image # Break out since limited to one series/image for now break # TODO: This needs to be updated once allowed to output multiple Image objects return study_selected_series_list[0].selected_series[0].image
[docs] def generate_voxel_data(self, series): """Applies rescale slope and rescale intercept to the pixels. Supports monochrome image only for now. Photometric Interpretation attribute, tag (0028,0004), is considered. Both MONOCHROME2 (IDENTITY) and MONOCHROME1 (INVERSE) result in an output image where The minimum sample value is intended to be displayed as black. Args: series: DICOM Series for which the pixel data needs to be extracted. Returns: A 3D numpy tensor representing the volumetric data. """ slices = series.get_sop_instances() # The sop_instance get_pixel_array() returns a 2D NumPy array with index order # of `HW`. The pixel array of all instances will be stacked along the first axis, # so the final 3D NumPy array will have index order of [DHW]. This is consistent # with the NumPy array returned from the ITK GetArrayViewFromImage on the image # loaded from the same DICOM series. vol_data = np.stack([s.get_pixel_array() for s in slices], axis=0) vol_data = vol_data.astype(np.int16) # For now we support monochrome image only, for which DICOM Photometric Interpretation # (0028,0004) has defined terms, MONOCHROME1 and MONOCHROME2, with the former being: # Pixel data represent a single monochrome image plane. The minimum sample value is # intended to be displayed as white after any VOI gray scale transformations have been # performed. See PS3.4. This value may be used only when Samples per Pixel (0028,0002) # has a value of 1. May be used for pixel data in a Native (uncompressed) or Encapsulated # (compressed) format; see Section 8.2 in PS3.5. # and for the latter "The minimum sample value is intended to be displayed as black" # # In this function, pixel data will be interpreted as if MONOCHROME2, hence inverting # MONOCHROME1 for the final voxel data. photometric_interpretation = ( slices[0].get_native_sop_instance().get("PhotometricInterpretation", "").strip().upper() ) presentation_lut_shape = slices[0].get_native_sop_instance().get("PresentationLUTShape", "").strip().upper() if not photometric_interpretation: logging.warning("Cannot get value of attribute Photometric Interpretation.") if photometric_interpretation != "MONOCHROME2": if photometric_interpretation == "MONOCHROME1" or presentation_lut_shape == "INVERSE": logging.debug("Applying INVERSE transformation as required for MONOCHROME1 image.") vol_data = np.amax(vol_data) - vol_data else: raise ValueError( f"Cannot process pixel data with Photometric Interpretation of {photometric_interpretation}." ) # Rescale Intercept and Slope attributes might be missing, but safe to assume defaults. try: intercept = slices[0][0x0028, 0x1052].value except KeyError: intercept = 0 try: slope = slices[0][0x0028, 0x1053].value except KeyError: slope = 1 if slope != 1: vol_data = slope * vol_data.astype(np.float64) vol_data = vol_data.astype(np.int16) vol_data += np.int16(intercept) return np.array(vol_data, dtype=np.int16)
[docs] def create_volumetric_image(self, vox_data, metadata): """Creates an instance of 3D image. Args: vox_data: A numpy array representing the volumetric data. metadata: DICOM attributes in a dictionary. Returns: An instance of Image object. """ image = Image(vox_data, metadata) return image
[docs] def prepare_series(self, series): """Computes the slice normal for each slice and then projects the first voxel of each slice on that slice normal. It computes the distance of that point from the origin of the patient coordinate system along the slice normal. It orders the slices in the series according to that distance. Args: series: An instance of DICOMSeries. """ if len(series._sop_instances) <= 1: series.depth_pixel_spacing = 1.0 # Default to 1, e.g. for CR image, similar to (Simple) ITK return slice_indices_to_be_removed = [] depth_pixel_spacing = 0.0 last_slice_normal = [0.0, 0.0, 0.0] for slice_index, slice in enumerate(series._sop_instances): distance = 0.0 point = [0.0, 0.0, 0.0] slice_normal = [0.0, 0.0, 0.0] slice_position = None cosines = None try: image_orientation_patient_de = slice[0x0020, 0x0037] if image_orientation_patient_de is not None: image_orientation_patient = image_orientation_patient_de.value cosines = image_orientation_patient except KeyError: pass try: image_poisition_patient_de = slice[0x0020, 0x0032] if image_poisition_patient_de is not None: image_poisition_patient = image_poisition_patient_de.value slice_position = image_poisition_patient except KeyError: pass distance = 0.0 if (cosines is not None) and (slice_position is not None): slice_normal[0] = cosines[1] * cosines[5] - cosines[2] * cosines[4] slice_normal[1] = cosines[2] * cosines[3] - cosines[0] * cosines[5] slice_normal[2] = cosines[0] * cosines[4] - cosines[1] * cosines[3] last_slice_normal = copy.deepcopy(slice_normal) i = 0 while i < 3: point[i] = slice_normal[i] * slice_position[i] i += 1 distance += point[0] + point[1] + point[2] series._sop_instances[slice_index].distance = distance series._sop_instances[slice_index].first_pixel_on_slice_normal = point else: print("going to removing slice ", slice_index) slice_indices_to_be_removed.append(slice_index) for sl_index, _ in enumerate(slice_indices_to_be_removed): del series._sop_instances[sl_index] series._sop_instances = sorted(series._sop_instances, key=lambda s: s.distance) series.depth_direction_cosine = copy.deepcopy(last_slice_normal) if len(series._sop_instances) > 1: p1 = series._sop_instances[0].first_pixel_on_slice_normal p2 = series._sop_instances[1].first_pixel_on_slice_normal depth_pixel_spacing = ( (p1[0] - p2[0]) * (p1[0] - p2[0]) + (p1[1] - p2[1]) * (p1[1] - p2[1]) + (p1[2] - p2[2]) * (p1[2] - p2[2]) ) depth_pixel_spacing = math.sqrt(depth_pixel_spacing) series.depth_pixel_spacing = depth_pixel_spacing s_1 = series._sop_instances[0] s_n = series._sop_instances[-1] num_slices = len(series._sop_instances) self.compute_affine_transform(s_1, s_n, num_slices, series)
[docs] def compute_affine_transform(self, s_1, s_n, n, series): """Computes the affine transform for this series. It does it in both DICOM Patient oriented coordinate system as well as the pne preferred by NIFTI standard. Accordingly, the two attributes dicom_affine_transform and nifti_affine_transform are stored in the series instance. The Image Orientation Patient contains two triplets, [rx ry rz cx cy cz], which encode direction cosines of the row and column of an image slice. The Image Position Patient of the first slice in a volume, [x1 y1 z1], is the x, y, z coordinates of the upper-left corner voxel of the slice. These two parameters define the location of the slice in PCS. To determine the location of a volume, the Image Position Patient of another slice is normally needed. In practice, we tend to use the position of the last slice in a volume, [xn yn zn]. The voxel size within the slice plane, [vr vc], is stored in object Pixel Spacing. Args: s_1: A first slice in the series. s_n: A last slice in the series. n: A number of slices in the series. series: An instance of DICOMSeries. """ m1 = np.arange(1, 17, dtype=float).reshape(4, 4) m2 = np.arange(1, 17, dtype=float).reshape(4, 4) image_orientation_patient = None try: image_orientation_patient_de = s_1[0x0020, 0x0037] if image_orientation_patient_de is not None: image_orientation_patient = image_orientation_patient_de.value except KeyError: pass rx = image_orientation_patient[0] ry = image_orientation_patient[1] rz = image_orientation_patient[2] cx = image_orientation_patient[3] cy = image_orientation_patient[4] cz = image_orientation_patient[5] vr = 0.0 vc = 0.0 try: pixel_spacing_de = s_1[0x0028, 0x0030] if pixel_spacing_de is not None: vr = pixel_spacing_de.value[0] vc = pixel_spacing_de.value[1] except KeyError: pass x1 = 0.0 y1 = 0.0 z1 = 0.0 xn = 0.0 yn = 0.0 zn = 0.0 ip1 = None ip2 = None try: ip1_de = s_1[0x0020, 0x0032] ipn_de = s_n[0x0020, 0x0032] ip1 = ip1_de.value ipn = ipn_de.value except KeyError: pass x1 = ip1[0] y1 = ip1[1] z1 = ip1[2] xn = ipn[0] yn = ipn[1] zn = ipn[2] m1[0, 0] = rx * vr m1[0, 1] = cx * vc m1[0, 2] = (xn - x1) / (n - 1) m1[0, 3] = x1 m1[1, 0] = ry * vr m1[1, 1] = cy * vc m1[1, 2] = (yn - y1) / (n - 1) m1[1, 3] = y1 m1[2, 0] = rz * vr m1[2, 1] = cz * vc m1[2, 2] = (zn - z1) / (n - 1) m1[2, 3] = z1 m1[3, 0] = 0 m1[3, 1] = 0 m1[3, 2] = 0 m1[3, 3] = 1 series.dicom_affine_transform = m1 m2[0, 0] = -rx * vr m2[0, 1] = -cx * vc m2[0, 2] = -(xn - x1) / (n - 1) m2[0, 3] = -x1 m2[1, 0] = -ry * vr m2[1, 1] = -cy * vc m2[1, 2] = -(yn - y1) / (n - 1) m2[1, 3] = -y1 m2[2, 0] = rz * vr m2[2, 1] = cz * vc m2[2, 2] = (zn - z1) / (n - 1) m2[2, 3] = z1 m2[3, 0] = 0 m2[3, 1] = 0 m2[3, 2] = 0 m2[3, 3] = 1 series.nifti_affine_transform = m2
[docs] def create_metadata(self, series) -> Dict: """Collects all relevant metadata from the DICOM Series and creates a dictionary. Args: series: An instance of DICOMSeries. Returns: An instance of a dictionary containing metadata for the volumetric image. """ # Set metadata with series properties that are not None. metadata = {} if series: metadata = self._get_instance_properties(series) return metadata
@staticmethod def _get_instance_properties(obj: object, not_none: bool = True) -> Dict: prop_dict = {} if obj: for attribute in [x for x in type(obj).__dict__ if isinstance(type(obj).__dict__[x], property)]: attr_val = getattr(obj, attribute, None) if not_none: if attr_val is not None: prop_dict[attribute] = attr_val else: prop_dict[attribute] = attr_val return prop_dict
def test(): from pathlib import Path from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator current_file_dir = Path(__file__).parent.resolve() data_path = current_file_dir.joinpath("../../../examples/ai_spleen_seg_data/dcm") loader = DICOMDataLoaderOperator() study_list = loader.load_data_to_studies(Path(data_path).absolute()) series_selector = DICOMSeriesSelectorOperator() study_selected_series_list = series_selector.filter(None, study_list) op = DICOMSeriesToVolumeOperator() image = op.convert_to_image(study_selected_series_list) print(f"Image NumPy array shape (index order DHW): {image.asnumpy().shape}") for k, v in image.metadata().items(): print(f"{(k)}: {(v)}") if __name__ == "__main__": test()