|
| 1 | +# Copyright 2021-2023 MONAI Consortium |
| 2 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 3 | +# you may not use this file except in compliance with the License. |
| 4 | +# You may obtain a copy of the License at |
| 5 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 6 | +# Unless required by applicable law or agreed to in writing, software |
| 7 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 8 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 9 | +# See the License for the specific language governing permissions and |
| 10 | +# limitations under the License. |
| 11 | + |
| 12 | +import logging |
| 13 | +from pathlib import Path |
| 14 | + |
| 15 | +from dicom_series_to_volume_operator_local import DICOMSeriesToVolumeOperator |
| 16 | + |
| 17 | +# Required for setting SegmentDescription attributes. Direct import as this is not part of App SDK package. |
| 18 | +from pydicom.sr.codedict import codes |
| 19 | + |
| 20 | +from monai.deploy.conditions import CountCondition |
| 21 | +from monai.deploy.core import AppContext, Application |
| 22 | +from monai.deploy.core.domain import Image |
| 23 | +from monai.deploy.core.io_type import IOType |
| 24 | +from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator |
| 25 | +from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription |
| 26 | +from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator |
| 27 | + |
| 28 | +# Use a local fixed version. from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator |
| 29 | +from monai.deploy.operators.monai_bundle_inference_operator import ( |
| 30 | + BundleConfigNames, |
| 31 | + IOMapping, |
| 32 | + MonaiBundleInferenceOperator, |
| 33 | +) |
| 34 | +from monai.deploy.operators.stl_conversion_operator import STLConversionOperator |
| 35 | +from reporter_operator import ExecutionStatusReporterOperator |
| 36 | + |
| 37 | + |
| 38 | +# @resource(cpu=1, gpu=1, memory="7Gi") |
| 39 | +# pip_packages can be a string that is a path(str) to requirements.txt file or a list of packages. |
| 40 | +# The monai pkg is not required by this class, instead by the included operators. |
| 41 | +class AISpleenSegApp(Application): |
| 42 | + """Demonstrates inference with built-in MONAI Bundle inference operator with DICOM files as input/output |
| 43 | +
|
| 44 | + This application loads a set of DICOM instances, select the appropriate series, converts the series to |
| 45 | + 3D volume image, performs inference with the built-in MONAI Bundle inference operator, including pre-processing |
| 46 | + and post-processing, save the segmentation image in a DICOM Seg OID in an instance file, and optionally the |
| 47 | + surface mesh in STL format. |
| 48 | +
|
| 49 | + Pertinent MONAI Bundle: |
| 50 | + https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation |
| 51 | +
|
| 52 | + Execution Time Estimate: |
| 53 | + With a Nvidia GV100 32GB GPU, for an input DICOM Series of 515 instances, the execution time is around |
| 54 | + 25 seconds with saving both DICOM Seg and surface mesh STL file, and 15 seconds with DICOM Seg only. |
| 55 | + """ |
| 56 | + |
| 57 | + def __init__(self, *args, status_callback=None, **kwargs): |
| 58 | + """Creates an application instance.""" |
| 59 | + self._logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__)) |
| 60 | + self._status_callback = status_callback |
| 61 | + super().__init__(*args, **kwargs) |
| 62 | + |
| 63 | + def run(self, *args, **kwargs): |
| 64 | + # This method calls the base class to run. Can be omitted if simply calling through. |
| 65 | + self._logger.info(f"Begin {self.run.__name__}") |
| 66 | + # The try...except block is removed as the reporter operator will handle status reporting. |
| 67 | + super().run(*args, **kwargs) |
| 68 | + self._logger.info(f"End {self.run.__name__}") |
| 69 | + |
| 70 | + def compose(self): |
| 71 | + """Creates the app specific operators and chain them up in the processing DAG.""" |
| 72 | + |
| 73 | + logging.info(f"Begin {self.compose.__name__}") |
| 74 | + |
| 75 | + # Use Commandline options over environment variables to init context. |
| 76 | + app_context: AppContext = Application.init_app_context(self.argv) |
| 77 | + app_input_path = Path(app_context.input_path) |
| 78 | + app_output_path = Path(app_context.output_path) |
| 79 | + |
| 80 | + # Create the custom operator(s) as well as SDK built-in operator(s). |
| 81 | + study_loader_op = DICOMDataLoaderOperator( |
| 82 | + self, CountCondition(self, 1), input_folder=app_input_path, name="study_loader_op" |
| 83 | + ) |
| 84 | + series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name="series_selector_op") |
| 85 | + series_to_vol_op = DICOMSeriesToVolumeOperator(self, name="series_to_vol_op") |
| 86 | + |
| 87 | + # Create the inference operator that supports MONAI Bundle and automates the inference. |
| 88 | + # The IOMapping labels match the input and prediction keys in the pre and post processing. |
| 89 | + # The model_name is optional when the app has only one model. |
| 90 | + # The bundle_path argument optionally can be set to an accessible bundle file path in the dev |
| 91 | + # environment, so when the app is packaged into a MAP, the operator can complete the bundle parsing |
| 92 | + # during init. |
| 93 | + |
| 94 | + config_names = BundleConfigNames(config_names=["inference"]) # Same as the default |
| 95 | + |
| 96 | + bundle_spleen_seg_op = MonaiBundleInferenceOperator( |
| 97 | + self, |
| 98 | + input_mapping=[IOMapping("image", Image, IOType.IN_MEMORY)], |
| 99 | + output_mapping=[IOMapping("pred", Image, IOType.IN_MEMORY)], |
| 100 | + app_context=app_context, |
| 101 | + bundle_config_names=config_names, |
| 102 | + name="bundle_spleen_seg_op", |
| 103 | + ) |
| 104 | + |
| 105 | + # Create DICOM Seg writer providing the required segment description for each segment with |
| 106 | + # the actual algorithm and the pertinent organ/tissue. The segment_label, algorithm_name, |
| 107 | + # and algorithm_version are of DICOM VR LO type, limited to 64 chars. |
| 108 | + # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html |
| 109 | + segment_descriptions = [ |
| 110 | + SegmentDescription( |
| 111 | + segment_label="Spleen", |
| 112 | + segmented_property_category=codes.SCT.Organ, |
| 113 | + segmented_property_type=codes.SCT.Spleen, |
| 114 | + algorithm_name="volumetric (3D) segmentation of the spleen from CT image", |
| 115 | + algorithm_family=codes.DCM.ArtificialIntelligence, |
| 116 | + algorithm_version="0.3.2", |
| 117 | + ) |
| 118 | + ] |
| 119 | + |
| 120 | + custom_tags = {"SeriesDescription": "AI generated Seg, not for clinical use."} |
| 121 | + |
| 122 | + dicom_seg_writer = DICOMSegmentationWriterOperator( |
| 123 | + self, |
| 124 | + segment_descriptions=segment_descriptions, |
| 125 | + custom_tags=custom_tags, |
| 126 | + output_folder=app_output_path, |
| 127 | + name="dicom_seg_writer", |
| 128 | + ) |
| 129 | + |
| 130 | + reporter_op = ExecutionStatusReporterOperator(self, status_callback=self._status_callback) |
| 131 | + |
| 132 | + # Create the processing pipeline, by specifying the source and destination operators, and |
| 133 | + # ensuring the output from the former matches the input of the latter, in both name and type. |
| 134 | + self.add_flow(study_loader_op, series_selector_op, {("dicom_study_list", "dicom_study_list")}) |
| 135 | + self.add_flow( |
| 136 | + series_selector_op, series_to_vol_op, {("study_selected_series_list", "study_selected_series_list")} |
| 137 | + ) |
| 138 | + self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {("image", "image")}) |
| 139 | + # Note below the dicom_seg_writer requires two inputs, each coming from a source operator. |
| 140 | + self.add_flow( |
| 141 | + series_selector_op, dicom_seg_writer, {("study_selected_series_list", "study_selected_series_list")} |
| 142 | + ) |
| 143 | + self.add_flow(bundle_spleen_seg_op, dicom_seg_writer, {("pred", "seg_image")}) |
| 144 | + # Create the surface mesh STL conversion operator and add it to the app execution flow, if needed, by |
| 145 | + # uncommenting the following couple lines. |
| 146 | + stl_conversion_op = STLConversionOperator( |
| 147 | + self, output_file=app_output_path.joinpath("stl/spleen.stl"), name="stl_conversion_op" |
| 148 | + ) |
| 149 | + self.add_flow(bundle_spleen_seg_op, stl_conversion_op, {("pred", "image")}) |
| 150 | + |
| 151 | + # Connect the reporter operator to the end of the pipeline. |
| 152 | + # It will be triggered after the DICOM SEG file is written. |
| 153 | + self.add_flow(stl_conversion_op, reporter_op, {("stl_bytes", "data")}) |
| 154 | + |
| 155 | + logging.info(f"End {self.compose.__name__}") |
| 156 | + |
| 157 | + |
| 158 | +# This is a sample series selection rule in JSON, simply selecting CT series. |
| 159 | +# If the study has more than 1 CT series, then all of them will be selected. |
| 160 | +# Please see more detail in DICOMSeriesSelectorOperator. |
| 161 | +Sample_Rules_Text = """ |
| 162 | +{ |
| 163 | + "selections": [ |
| 164 | + { |
| 165 | + "name": "CT Series", |
| 166 | + "conditions": { |
| 167 | + "StudyDescription": "(.*?)", |
| 168 | + "Modality": "(?i)CT", |
| 169 | + "SeriesDescription": "(.*?)" |
| 170 | + } |
| 171 | + } |
| 172 | + ] |
| 173 | +} |
| 174 | +""" |
| 175 | + |
| 176 | +if __name__ == "__main__": |
| 177 | + # Creates the app and test it standalone. When running is this mode, please note the following: |
| 178 | + # -m <model file>, for model file path |
| 179 | + # -i <DICOM folder>, for input DICOM CT series folder |
| 180 | + # -o <output folder>, for the output folder, default $PWD/output |
| 181 | + # e.g. |
| 182 | + # monai-deploy exec app.py -i input -m model/model.ts |
| 183 | + # |
| 184 | + logging.info(f"Begin {__name__}") |
| 185 | + AISpleenSegApp().run() |
| 186 | + logging.info(f"End {__name__}") |
0 commit comments