Rebranding MEDimage to MEDiml

.readthedocs.yaml

@@ -1,5 +1,9 @@
 version: 2
 
+sphinx:
+  # Path to Sphinx configuration file.
+  configuration: docs/conf.py
+
 # Set the version of Python
 build:
   os: ubuntu-20.04

MEDimage/biomarkers/BatchExtractorTexturalFilters.py → MEDiml/biomarkers/BatchExtractorTexturalFilters.py

@@ -15,7 +15,7 @@
 import ray
 from tqdm import trange
 
-import MEDimage
+import MEDiml
 
 
 class BatchExtractorTexturalFilters(object):
@@ -72,7 +72,7 @@ def __init__(
     def __load_and_process_params(self) -> Dict:
         """Load and process the computing & batch parameters from JSON file"""
         # Load json parameters
-        im_params = MEDimage.utils.json_utils.load_json(self._path_params)
+        im_params = MEDiml.utils.json_utils.load_json(self._path_params)
 
         # Update class attributes
         self.roi_types.extend(im_params['roi_types'])
@@ -95,7 +95,7 @@ def __compute_radiomics_one_patient(
         Computes all radiomics features (Texture & Non-texture) for one patient/scan
 
         Args:
-            name_patient(str): scan or patient full name. It has to respect the MEDimage naming convention:
+            name_patient(str): scan or patient full name. It has to respect the MEDiml naming convention:
                 PatientID__ImagingScanName.ImagingModality.npy
             roi_name(str): name of the ROI that will  be used in computation.
             im_params(Dict): Dict of parameters/settings that will be used in the processing and computation.
@@ -136,7 +136,7 @@ def __compute_radiomics_one_patient(
         # Load MEDscan instance
         try:
             with open(self._path_read / name_patient, 'rb') as f: medscan = pickle.load(f)
-            medscan = MEDimage.MEDscan(medscan)
+            medscan = MEDiml.MEDscan(medscan)
         except Exception as e:
             logging.error(f"\n ERROR LOADING PATIENT {name_patient}:\n {e}")
             return None
@@ -148,7 +148,7 @@ def __compute_radiomics_one_patient(
         # Get ROI (region of interest)
         logging.info("\n--> Extraction of ROI mask:")
         try:
-            vol_obj_init, roi_obj_init = MEDimage.processing.get_roi_from_indexes(
+            vol_obj_init, roi_obj_init = MEDiml.processing.get_roi_from_indexes(
                 medscan,
                 name_roi=roi_name,
                 box_string=medscan.params.process.box_string
@@ -164,7 +164,7 @@ def __compute_radiomics_one_patient(
 
         # Interpolation
         # Intensity Mask
-        vol_obj = MEDimage.processing.interp_volume(
+        vol_obj = MEDiml.processing.interp_volume(
             medscan=medscan,
             vol_obj_s=vol_obj_init,
             vox_dim=medscan.params.process.scale_non_text,
@@ -175,7 +175,7 @@ def __compute_radiomics_one_patient(
             box_string=medscan.params.process.box_string
         )
         # Morphological Mask
-        roi_obj_morph = MEDimage.processing.interp_volume(
+        roi_obj_morph = MEDiml.processing.interp_volume(
             medscan=medscan,
             vol_obj_s=roi_obj_init,
             vox_dim=medscan.params.process.scale_non_text,
@@ -189,14 +189,14 @@ def __compute_radiomics_one_patient(
         # Re-segmentation
         # Intensity mask range re-segmentation
         roi_obj_int = deepcopy(roi_obj_morph)
-        roi_obj_int.data = MEDimage.processing.range_re_seg(
+        roi_obj_int.data = MEDiml.processing.range_re_seg(
             vol=vol_obj.data, 
             roi=roi_obj_int.data,
             im_range=medscan.params.process.im_range
         )
         # Intensity mask outlier re-segmentation
         roi_obj_int.data = np.logical_and(
-            MEDimage.processing.outlier_re_seg(
+            MEDiml.processing.outlier_re_seg(
                 vol=vol_obj.data, 
                 roi=roi_obj_int.data, 
                 outliers=medscan.params.process.outliers
@@ -216,7 +216,7 @@ def __compute_radiomics_one_patient(
 
         # ROI Extraction :
         try:
-            vol_int_re = MEDimage.processing.roi_extract(
+            vol_int_re = MEDiml.processing.roi_extract(
                 vol=vol_obj.data, 
                 roi=roi_obj_int.data
             )
@@ -228,7 +228,7 @@ def __compute_radiomics_one_patient(
         try:
             if medscan.params.process.user_set_min_value is None:
                 medscan.params.process.user_set_min_value = np.nanmin(vol_int_re)
-            vol_obj_all_features = MEDimage.filters.apply_filter(
+            vol_obj_all_features = MEDiml.filters.apply_filter(
                 medscan, 
                 vol_int_re, 
                 user_set_min_val=medscan.params.process.user_set_min_value
@@ -311,7 +311,7 @@ def __compute_radiomics_one_patient(
     @ray.remote
     def __compute_radiomics_filtered_volume(
             self,
-            medscan: MEDimage.MEDscan,
+            medscan: MEDiml.MEDscan,
             vol_obj,
             roi_obj_int,
             roi_obj_morph,
@@ -338,7 +338,7 @@ def __compute_radiomics_filtered_volume(
 
         # Morphological features extraction
         try:
-            morph = MEDimage.biomarkers.morph.extract_all(
+            morph = MEDiml.biomarkers.morph.extract_all(
                 vol=vol_obj.data, 
                 mask_int=roi_obj_int.data, 
                 mask_morph=roi_obj_morph.data,
@@ -351,7 +351,7 @@ def __compute_radiomics_filtered_volume(
 
         # Local intensity features extraction
         try:
-            local_intensity = MEDimage.biomarkers.local_intensity.extract_all(
+            local_intensity = MEDiml.biomarkers.local_intensity.extract_all(
                 img_obj=vol_obj.data,
                 roi_obj=roi_obj_int.data,
                 res=medscan.params.process.scale_non_text,
@@ -363,7 +363,7 @@ def __compute_radiomics_filtered_volume(
 
         # statistical features extraction
         try:
-            stats = MEDimage.biomarkers.stats.extract_all(
+            stats = MEDiml.biomarkers.stats.extract_all(
                 vol=vol_int_re,
                 intensity_type=medscan.params.process.intensity_type
             )
@@ -372,7 +372,7 @@ def __compute_radiomics_filtered_volume(
             stats = None
 
         # Intensity histogram equalization of the imaging volume
-        vol_quant_re, _ = MEDimage.processing.discretize(
+        vol_quant_re, _ = MEDiml.processing.discretize(
             vol_re=vol_int_re,
             discr_type=medscan.params.process.ih['type'], 
             n_q=medscan.params.process.ih['val'], 
@@ -381,7 +381,7 @@ def __compute_radiomics_filtered_volume(
 
         # Intensity histogram features extraction
         try:
-            int_hist = MEDimage.biomarkers.intensity_histogram.extract_all(
+            int_hist = MEDiml.biomarkers.intensity_histogram.extract_all(
                 vol=vol_quant_re
             )
         except Exception as e:
@@ -391,7 +391,7 @@ def __compute_radiomics_filtered_volume(
         # Intensity histogram equalization of the imaging volume
         if medscan.params.process.ivh and 'type' in medscan.params.process.ivh and 'val' in medscan.params.process.ivh:
             if medscan.params.process.ivh['type'] and medscan.params.process.ivh['val']:
-                vol_quant_re, wd = MEDimage.processing.discretize(
+                vol_quant_re, wd = MEDiml.processing.discretize(
                     vol_re=vol_int_re,
                     discr_type=medscan.params.process.ivh['type'], 
                     n_q=medscan.params.process.ivh['val'], 
@@ -404,7 +404,7 @@ def __compute_radiomics_filtered_volume(
 
         # Intensity volume histogram features extraction
         try:
-            int_vol_hist = MEDimage.biomarkers.int_vol_hist.extract_all(
+            int_vol_hist = MEDiml.biomarkers.int_vol_hist.extract_all(
                 medscan=medscan,
                 vol=vol_quant_re,
                 vol_int_re=vol_int_re, 
@@ -449,7 +449,7 @@ def __compute_radiomics_filtered_volume(
 
             # Discretisation :
             try:
-                vol_quant_re, _ = MEDimage.processing.discretize(
+                vol_quant_re, _ = MEDiml.processing.discretize(
                     vol_re=vol_int_re,
                     discr_type=medscan.params.process.algo[a], 
                     n_q=medscan.params.process.gray_levels[a][n], 
@@ -461,7 +461,7 @@ def __compute_radiomics_filtered_volume(
 
             # GLCM features extraction
             try:
-                glcm = MEDimage.biomarkers.glcm.extract_all(
+                glcm = MEDiml.biomarkers.glcm.extract_all(
                     vol=vol_quant_re, 
                     dist_correction=medscan.params.radiomics.glcm.dist_correction
                 )
@@ -471,7 +471,7 @@ def __compute_radiomics_filtered_volume(
 
             # GLRLM features extraction
             try:
-                glrlm = MEDimage.biomarkers.glrlm.extract_all(
+                glrlm = MEDiml.biomarkers.glrlm.extract_all(
                     vol=vol_quant_re,
                     dist_correction=medscan.params.radiomics.glrlm.dist_correction
                 )
@@ -481,14 +481,14 @@ def __compute_radiomics_filtered_volume(
 
             # GLSZM features extraction
             try:
-                glszm = MEDimage.biomarkers.glszm.extract_all(vol=vol_quant_re)
+                glszm = MEDiml.biomarkers.glszm.extract_all(vol=vol_quant_re)
             except Exception as e:
                 logging.error(f'PROBLEM WITH COMPUTATION OF GLSZM FEATURES {e}')
                 glszm = None
 
             # GLDZM features extraction
             try:
-                gldzm = MEDimage.biomarkers.gldzm.extract_all(
+                gldzm = MEDiml.biomarkers.gldzm.extract_all(
                     vol_int=vol_quant_re, 
                     mask_morph=roi_obj_morph.data
                 )
@@ -498,7 +498,7 @@ def __compute_radiomics_filtered_volume(
 
             # NGTDM features extraction
             try:
-                ngtdm = MEDimage.biomarkers.ngtdm.extract_all(
+                ngtdm = MEDiml.biomarkers.ngtdm.extract_all(
                     vol=vol_quant_re, 
                     dist_correction=medscan.params.radiomics.ngtdm.dist_correction
                 )
@@ -508,7 +508,7 @@ def __compute_radiomics_filtered_volume(
 
             # NGLDM features extraction
             try:
-                ngldm = MEDimage.biomarkers.ngldm.extract_all(vol=vol_quant_re)
+                ngldm = MEDiml.biomarkers.ngldm.extract_all(vol=vol_quant_re)
             except Exception as e:
                 logging.error(f'PROBLEM WITH COMPUTATION OF NGLDM FEATURES {e}')
                 ngldm = None
@@ -605,8 +605,8 @@ def __compute_radiomics_tables(
             wildcard = '*_' + scan + '(' + roi_type + ')*.json'
 
             # Create radiomics table
-            radiomics_table_dict = MEDimage.utils.create_radiomics_table(
-                MEDimage.utils.get_file_paths(self._path_save / f'features({roi_label})', wildcard),
+            radiomics_table_dict = MEDiml.utils.create_radiomics_table(
+                MEDiml.utils.get_file_paths(self._path_save / f'features({roi_label})', wildcard),
                 im_space, 
                 log_file
             )
@@ -617,7 +617,7 @@ def __compute_radiomics_tables(
             np.save(save_path, [radiomics_table_dict])
 
             # Create CSV table and Definitions
-            MEDimage.utils.write_radiomics_csv(save_path)
+            MEDiml.utils.write_radiomics_csv(save_path)
 
             logging.info(f"DONE\n {time() - start}\n")
 
@@ -740,13 +740,13 @@ def __batch_all_tables(self, im_params: Dict):
                 label = self.roi_type_labels[r]
                 wildcard = '*' + label + '*.json'
                 roi_type = self.roi_types[r] + '_' + self.glcm_features[f_idx]
-                file_paths = MEDimage.utils.get_file_paths(self._path_save / f'features({roi_type})', wildcard)
+                file_paths = MEDiml.utils.get_file_paths(self._path_save / f'features({roi_type})', wildcard)
                 n_files = len(file_paths)
                 scans = [0] * n_files
                 modalities = [0] * n_files
                 for f in range(0, n_files):
                     rad_file_name = file_paths[f].stem
-                    scans[f] = MEDimage.utils.get_scan_name_from_rad_name(rad_file_name)
+                    scans[f] = MEDiml.utils.get_scan_name_from_rad_name(rad_file_name)
                     modalities[f] = rad_file_name.split('.')[1]
                 scans = s = (np.unique(np.array(scans))).tolist()
                 n_scans = len(scans)
@@ -755,12 +755,12 @@ def __batch_all_tables(self, im_params: Dict):
                     scan = scans[s]
                     modality = modalities[s]
                     wildcard = '*' + scan + '(' + label + ')*.json'
-                    file_paths = MEDimage.utils.get_file_paths(self._path_save / f'features({roi_type})', wildcard)
+                    file_paths = MEDiml.utils.get_file_paths(self._path_save / f'features({roi_type})', wildcard)
                     n_files = len(file_paths)
 
                     # Finding the images spaces for a test file (assuming that all
                     # files for a given scan and roi_type_label have the same image spaces
-                    radiomics = MEDimage.utils.json_utils.load_json(file_paths[0])
+                    radiomics = MEDiml.utils.json_utils.load_json(file_paths[0])
                     im_spaces = [key for key in radiomics.keys()]
                     im_spaces = im_spaces[:-1]
                     n_im_spaces = len(im_spaces)