Fixing issue with model names and brain mask interaction [skip ci]

.github/workflows/build_macos.yml

@@ -80,8 +80,8 @@ jobs:
     - name: Make installer
       run: |
         git clone https://github.com/dbouget/quickpkg.git
-        quickpkg/quickpkg dist/Raidionics.app --output Raidionics-1.3.1-macOS.pkg
-        cp -r Raidionics-1.3.1-macOS.pkg dist/Raidionics-1.3.1-macOS-x86_64.pkg
+        quickpkg/quickpkg dist/Raidionics.app --output Raidionics-1.3.2-macOS.pkg
+        cp -r Raidionics-1.3.2-macOS.pkg dist/Raidionics-1.3.2-macOS-x86_64.pkg
 
     - name: Upload package
       uses: actions/upload-artifact@v4

.github/workflows/build_macos_arm.yml

@@ -91,8 +91,8 @@ jobs:
     - name: Make installer
       run: |
         git clone https://github.com/dbouget/quickpkg.git
-        quickpkg/quickpkg dist/Raidionics.app --output Raidionics-1.3.1-macOS.pkg
-        cp -r Raidionics-1.3.1-macOS.pkg dist/Raidionics-1.3.1-macOS-arm64.pkg
+        quickpkg/quickpkg dist/Raidionics.app --output Raidionics-1.3.2-macOS.pkg
+        cp -r Raidionics-1.3.2-macOS.pkg dist/Raidionics-1.3.2-macOS-arm64.pkg
 
     - name: Upload package
       uses: actions/upload-artifact@v4

.github/workflows/build_ubuntu.yml

@@ -115,7 +115,7 @@ jobs:
         cp -r dist/Raidionics assets/Raidionics_ubuntu/usr/local/bin
         dpkg-deb --build --root-owner-group assets/Raidionics_ubuntu
         ls -la
-        cp -r assets/Raidionics_ubuntu.deb dist/Raidionics-1.3.1-ubuntu.deb
+        cp -r assets/Raidionics_ubuntu.deb dist/Raidionics-1.3.2-ubuntu.deb
 
     - name: Upload package
       uses: actions/upload-artifact@v4

.github/workflows/build_windows.yml

@@ -69,7 +69,7 @@ jobs:
     - name: Make installer
       run: |
         makensis.exe assets/Raidionics.nsi
-        cp -r assets/Raidionics-1.3.1-win.exe dist/Raidionics-1.3.1-win.exe
+        cp -r assets/Raidionics-1.3.2-win.exe dist/Raidionics-1.3.2-win.exe
 
     - name: Upload package
       uses: actions/upload-artifact@v4

README.md

@@ -8,6 +8,7 @@
 [![Paper](https://zenodo.org/badge/DOI/10.1038/s41598-023-42048-7.svg)](https://doi.org/10.1038/s41598-023-42048-7)
 [![codecov](https://codecov.io/gh/raidionics/Raidionics/branch/master/graph/badge.svg?token=ZSPQVR7RKX)](https://codecov.io/gh/raidionics/Raidionics)
 [![GitHub release](https://img.shields.io/github/v/release/raidionics/raidionics?sort=semver)](https://github.com/raidionics/raidionics/releases)
+<a target="_blank" href="https://huggingface.co/spaces/dbouget/raidionics"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-yellow.svg"></a>
 
 **Raidionics** was developed by SINTEF Medical Image Analysis. A paper presenting the software and some benchmarks has been published in [Scientific Reports](https://doi.org/10.1038/s41598-023-42048-7).
 

assets/Raidionics.nsi

@@ -1,8 +1,8 @@
 !define APP_NAME "Raidionics"
 !define COMP_NAME "SINTEF"
-!define VERSION "1.3.1"
+!define VERSION "1.3.2"
 !define DESCRIPTION "Application"
-!define INSTALLER_NAME "Raidionics-1.3.1-win.exe"
+!define INSTALLER_NAME "Raidionics-1.3.2-win.exe"
 !define MAIN_APP_EXE "Raidionics.exe"
 !define INSTALL_TYPE "SetShellVarContext current"
 !define REG_ROOT "HKLM"

assets/main.spec

@@ -84,7 +84,7 @@ if sys.platform == "darwin":
                     'CFBundleIdentifier': 'Raidionics',
                     'CFBundleInfoDictionaryVersion': '6.0',
                     'CFBundleName': 'Raidionics',
-                    'CFBundleVersion': '1.3.1',
+                    'CFBundleVersion': '1.3.2',
                     'CFBundlePackageType': 'APPL',
                     'LSBackgroundOnly': 'false',
                 },

assets/main_arm.spec

@@ -89,7 +89,7 @@ if sys.platform == "darwin":
                     'CFBundleIdentifier': 'Raidionics',
                     'CFBundleInfoDictionaryVersion': '6.0',
                     'CFBundleName': 'Raidionics',
-                    'CFBundleVersion': '1.3.1',
+                    'CFBundleVersion': '1.3.2',
                     'CFBundlePackageType': 'APPL',
                     'LSBackgroundOnly': 'false',
                 },

gui/SinglePatientComponent/PatientResultsSidePanel/SinglePatientResultsWidget.py

@@ -414,13 +414,23 @@ def on_standardized_report_imported(self, report_uid: str) -> None:
                 report = None
                 report = TumorCharacteristicsWidget(patient_uid=self.uid, report_uid=report_uid, structure_name=c)
                 report_visible_name = f"Features: {c} - {report_structure.timestamp_folder_name}"
+                ritems = [self.results_selector_combobox.itemText(i) for i in range(self.results_selector_combobox.count())]
 
-                if report:
-                    self.report_widgets[report_uid] = report
-                    self.results_display_stackedwidget.addWidget(report)
-                    self.results_selector_combobox.addItem(report_visible_name)
-                    report.resizeRequested.connect(self.resizeRequested)
-                    self.resizeRequested.emit()
+                if not report:
+                    return
+
+                if report_visible_name in ritems:
+                    rind = self.results_selector_combobox.findText(report_visible_name)
+                    dkey = list(self.report_widgets.keys())[rind]
+                    self.results_display_stackedwidget.removeWidget(self.report_widgets[dkey])
+                    self.report_widgets[dkey].deleteLater()
+                    self.report_widgets.pop(dkey)
+
+                self.report_widgets[report_uid] = report
+                self.results_display_stackedwidget.addWidget(report)
+                self.results_selector_combobox.addItem(report_visible_name)
+                report.resizeRequested.connect(self.resizeRequested)
+                self.resizeRequested.emit()
 
     def on_size_request(self):
         self.resizeRequested.emit()

gui/UtilsWidgets/CustomQDialog/ResearchCommunityDialog.py

@@ -159,7 +159,7 @@ def __set_interface(self):
 
         self.brats_widget = HospitalContributorWidget(self)
         self.brats_widget.set_hospital_name("The BraTS challenge 2023/2024")
-        self.brats_widget.set_hospital_participants("""<a href=https://www.synapse.org/Synapse:syn64153130/wiki/>Official website</a>""")
+        self.brats_widget.set_hospital_participants("""<a href="https://www.synapse.org/Synapse:syn64153130/wiki">Official website</a>""")
         self.brats_widget.set_logo_icon(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                                         '../../Images/brats-challenge-logo.png'))
         self.main_scrollarea_layout.addWidget(self.brats_widget, 5, 0, 1, 1)
@@ -237,7 +237,8 @@ def __set_interface(self):
         self.hospital_name_label = QLabel()
         self.hospital_name_label.setTextInteractionFlags(Qt.TextSelectableByMouse)
         self.hospital_participants_label = QLabel()
-        self.hospital_participants_label.setTextInteractionFlags(Qt.TextSelectableByMouse)
+        self.hospital_participants_label.setTextInteractionFlags(Qt.TextSelectableByMouse | Qt.TextBrowserInteraction)
+        self.hospital_participants_label.setOpenExternalLinks(True)
 
         self.hospital_location_layout = QVBoxLayout()
         self.hospital_location_layout.setSpacing(0)

utils/backend_logic.py

@@ -257,15 +257,15 @@ def generate_surrogate_folder(patient_parameters: PatientParameters, output_fold
                     for anno in manual_annos:
                         shutil.copyfile(src=patient_parameters.get_annotation_by_uid(anno).usable_input_filepath,
                                         dst=os.path.join(surrogate_folder, "T" + str(ts_object.order), os.path.basename(
-                                            patient_parameters.get_mri_by_uid(im).usable_input_filepath[:-7] + '-label_' + str(c) + '.nii.gz')))
+                                            patient_parameters.get_mri_by_uid(im).usable_input_filepath[:-7] + '-label_' + c.name + '.nii.gz')))
                 else:
                     annos = patient_parameters.get_specific_annotations_for_mri(mri_volume_uid=im,
                                                                                 generation_type=AnnotationGenerationType.Automatic,
                                                                                 annotation_class=c)
                     for anno in annos:
                         shutil.copyfile(src=patient_parameters.get_annotation_by_uid(anno).usable_input_filepath,
                                         dst=os.path.join(surrogate_folder, "T" + str(ts_object.order), os.path.basename(
-                                            patient_parameters.get_mri_by_uid(im).usable_input_filepath[:-7] + '-label_' + str(c) + '.nii.gz')))
+                                            patient_parameters.get_mri_by_uid(im).usable_input_filepath[:-7] + '-label_' + c.name + '.nii.gz')))
 
     except Exception:
         logging.error('Pipeline surrogate folder creation failed with: \n{}'.format(traceback.format_exc()))

utils/data_structures/AnnotationStructure.py

@@ -29,6 +29,7 @@ class AnnotationClassType(Enum):
     Cavity = 4, 'Cavity'
     TumorCE = 5, 'Contrast-Enhancing Tumor'
     WT = 6, 'Whole Tumor'  # Corresponds to the sum of the tumor-CE, necrosis, and edema
+    Edema = 7, 'Surrounding non-enhancing FLAIR changes'
     # @TODO. Is FLAIRChanges the whole tumor and should we support an edema category in addition?
 
     Lungs = 100, 'Lungs'

utils/data_structures/PatientParametersStructure.py

@@ -949,6 +949,14 @@ def get_all_annotation_uids_for_radiological_volume(self, radiological_uid: str)
                 res.append(im)
         return res
 
+    def get_all_reports_for_mri_and_type(self, mri_volume_uid: str, report_type: str) -> List[ReportingStructure]:
+        res = []
+        for r in list(self._reportings.keys()):
+            if (self._reportings[r].parent_mri_uid == mri_volume_uid and
+                    self._reportings[r].get_report_task_str() == report_type):
+                res.append(self._reportings[r])
+        return res
+
     def get_all_atlases_for_mri(self, mri_volume_uid: str) -> List[str]:
         """
         Convenience method for collecting all atlas objects linked to a specific MRI volume.

utils/logic/PipelineCreationHandler.py

@@ -135,6 +135,17 @@ def __create_preop_segmentation_pipeline(tumor_type: str) -> dict:
         pip[pip_num]["description"] = "Identifying the best necrosis segmentation model for existing inputs"
         download_model(model_name='MRI_Necrosis')
 
+        pip_num_int = pip_num_int + 1
+        pip_num = str(pip_num_int)
+        pip[pip_num] = {}
+        pip[pip_num]["task"] = 'Model selection'
+        pip[pip_num]["model"] = 'MRI_SNFH'
+        pip[pip_num]["timestamp"] = 0
+        pip[pip_num]["format"] = "thresholding"
+        pip[pip_num]["description"] = ("Identifying the best surrounding non-enhancing FLAIR hyperintensity (SNFH) "
+                                       "segmentation model for existing inputs")
+        download_model(model_name='MRI_SNFH')
+
     pip_num_int = pip_num_int + 1
     pip_num = str(pip_num_int)
     pip[pip_num] = {}
@@ -238,6 +249,17 @@ def __create_postop_segmentation_pipeline(tumor_type: str) -> dict:
         pip[pip_num]["description"] = "Identifying the best rest enhancing tumor segmentation model for existing inputs"
         download_model(model_name='MRI_TumorCE_Postop')
 
+        pip_num_int = pip_num_int + 1
+        pip_num = str(pip_num_int)
+        pip[pip_num] = {}
+        pip[pip_num]["task"] = 'Model selection'
+        pip[pip_num]["model"] = 'MRI_SNFH'
+        pip[pip_num]["timestamp"] = postop_ts
+        pip[pip_num]["format"] = "thresholding"
+        pip[pip_num]["description"] = ("Identifying the best surrounding non-enhancing FLAIR hyperintensity (SNFH) "
+                                       "segmentation model for existing inputs")
+        download_model(model_name='MRI_SNFH')
+
     pip_num_int = pip_num_int + 1
     pip_num = str(pip_num_int)
     pip[pip_num] = {}
@@ -312,6 +334,17 @@ def __create_preop_reporting_pipeline(tumor_type: str) -> dict:
         pip[pip_num]["description"] = "Identifying the best necrosis segmentation model for existing inputs"
         download_model(model_name='MRI_Necrosis')
 
+        pip_num_int = pip_num_int + 1
+        pip_num = str(pip_num_int)
+        pip[pip_num] = {}
+        pip[pip_num]["task"] = 'Model selection'
+        pip[pip_num]["model"] = 'MRI_SNFH'
+        pip[pip_num]["timestamp"] = 0
+        pip[pip_num]["format"] = "thresholding"
+        pip[pip_num]["description"] = ("Identifying the best surrounding non-enhancing FLAIR hyperintensity (SNFH) "
+                                       "segmentation model for existing inputs")
+        download_model(model_name='MRI_SNFH')
+
     pip_num_int = pip_num_int + 1
     pip_num = str(pip_num_int)
     pip[pip_num] = {}
@@ -362,6 +395,17 @@ def __create_postop_reporting_pipeline(tumor_type: str) -> dict:
         pip[pip_num]["description"] = "Identifying the best tumor core segmentation model for existing inputs"
         download_model(model_name='MRI_TumorCE_Postop')
 
+        pip_num_int = pip_num_int + 1
+        pip_num = str(pip_num_int)
+        pip[pip_num] = {}
+        pip[pip_num]["task"] = 'Model selection'
+        pip[pip_num]["model"] = 'MRI_SNFH'
+        pip[pip_num]["timestamp"] = postop_ts
+        pip[pip_num]["format"] = "thresholding"
+        pip[pip_num]["description"] = ("Identifying the best surrounding non-enhancing FLAIR hyperintensity (SNFH) "
+                                       "segmentation model for existing inputs")
+        download_model(model_name='MRI_SNFH')
+
     pip_num_int = pip_num_int + 1
     pip_num = str(pip_num_int)
     pip[pip_num] = {}

utils/logic/PipelineResultsCollector.py

@@ -7,6 +7,7 @@
 import pandas as pd
 import glob
 
+from tmp_dependencies.utils.data_structures.ReportingStructure import ReportingType
 from utils.data_structures.UserPreferencesStructure import UserPreferencesStructure
 from utils.data_structures.MRIVolumeStructure import MRISequenceType
 from utils.data_structures.AnnotationStructure import AnnotationClassType, AnnotationGenerationType
@@ -414,11 +415,19 @@ def collect_results(patient_parameters, pipeline):
                 report_filename = os.path.join(patient_parameters.output_folder, 'reporting', 'reporting',
                                                "T" + str(timestamp), 'neuro_clinical_report.json')
 
-                # @TODO. Hard-coded for contrast-enhanced, will have to make it adjustable (should the base image be
-                # returned from the backend?
-                # parent_mri_uid = patient_parameters.get_all_mri_volumes_for_timestamp(timestamp_uid=patient_parameters.get_timestamp_by_order(order=timestamp).unique_id)
-                parent_mri_uid = patient_parameters.get_all_mri_volumes_for_sequence_type_and_timestamp(sequence_type=MRISequenceType.T1c,
-                    timestamp_order=timestamp)
+                parent_mri_uid = []
+                if pip_step["tumor_type"] == "contrast-enhancing":
+                    parent_mri_uid = patient_parameters.get_all_mri_volumes_for_sequence_type_and_timestamp(
+                        sequence_type=MRISequenceType.T1c,
+                        timestamp_order=timestamp)
+                elif pip_step["tumor_type"] == "non contrast-enhancing":
+                    # @TODO. In the future, it might be T2 is the base image, should be adjustable?
+                    parent_mri_uid = patient_parameters.get_all_mri_volumes_for_sequence_type_and_timestamp(
+                        sequence_type=MRISequenceType.FLAIR,
+                        timestamp_order=timestamp)
+                else:
+                    logging.warning(f"[PipelineResultsCollector] Use-case not handled for updating a timestamp report"
+                                    f" for the following tumor type:{pip_step['tumor_type']}.")
                 if len(parent_mri_uid) == 0:
                     continue
                 parent_mri_uid = parent_mri_uid[0]
@@ -442,10 +451,17 @@ def collect_results(patient_parameters, pipeline):
                 shutil.move(report_filename_txt, dest_file_txt)
 
                 if os.path.exists(dest_file):  # Should always exist
-                    report_uid, error_msg = patient_parameters.import_report(dest_file, dest_ts_object.unique_id)
-                    #@TODO. Maybe the reporting type could be named differently?
-                    patient_parameters.reportings[report_uid].set_reporting_type("Tumor characteristics")
-                    patient_parameters.reportings[report_uid].parent_mri_uid = parent_mri_uid
+                    # If a report was previously computed, it should simply be updated
+                    existing_reports = patient_parameters.get_all_reports_for_mri_and_type(mri_volume_uid=parent_mri_uid,
+                                                                                           report_type=str(ReportingType.Features))
+                    if len(existing_reports) == 0:
+                        report_uid, error_msg = patient_parameters.import_report(dest_file, dest_ts_object.unique_id)
+                        patient_parameters.reportings[report_uid].set_reporting_type("Tumor characteristics")
+                        patient_parameters.reportings[report_uid].parent_mri_uid = parent_mri_uid
+                    else:
+                        # @TODO. It shouldn't be allowed with more than 1, have to improve!
+                        existing_report = existing_reports[0]
+                        report_uid = existing_report.unique_id
                     results['Report'].append(report_uid)
             elif pip_step["task"] == "Surgical reporting":
                 report_filename = os.path.join(patient_parameters.output_folder, 'reporting', 'reporting',