added patient id splits

SPIDER.py

@@ -23,7 +23,7 @@ from typing import Dict, List, Optional, Set
 import numpy as np
 
 import datasets
-
+import SimpleITK as sitk
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -160,12 +160,19 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         
-        # Generate train/validate/test partitions of patient IDs
+        # Config params
+        #TODO: make hardcoded values dynamic
         np.random.seed(9999)
-        N_PATIENTS = 257 #TODO: make hardcoded values dynamic
+        N_PATIENTS = 257 
         VALIDATE_SHARE = 0.3
         TEST_SHARE = 0.2
         TRAIN_SHARE = (1.0 - VALIDATE_SHARE - TEST_SHARE)
+        
+        scan_types = ['t1', 't2', 't2_SPACE']
+        
+        
+        
+        # Generate train/validate/test partitions of patient IDs
         partition = np.random.choice(
             ['train', 'dev', 'test'],
             p=[TRAIN_SHARE, VALIDATE_SHARE, TEST_SHARE],
@@ -176,9 +183,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         validate_ids = set(patient_ids[partition == 'dev'])
         test_ids = set(patient_ids[partition == 'test'])
         assert len(train_ids.union(validate_ids, test_ids)) == N_PATIENTS
-        
-        
-        
+
         # Import patient/scanner data and radiological gradings data
         overview_data = import_csv_data(paths_dict['overview'])
         grades_data = import_csv_data(paths_dict['gradings'])
@@ -196,17 +201,73 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         ]
         assert len(mask_files) > 0, "No mask files found--check directory path."
         
-        images = []
-        masks = []
+        # Filter image and mask data based on scan types
+        image_files = [
+            file for file in image_files 
+            if any(scan_type in file for scan_type in scan_types)
+        ]
+
+        mask_files = [
+            file for file in mask_files 
+            if any(scan_type in file for scan_type in scan_types)
+        ]
+        
+        # Subset train/validation/test partition images and mask files
         if split == 'train':
-            for patient_id in train_ids:
-                
-            
+            subset_ids = train_ids
         elif split == 'validate':
-            
+            subset_ids = validate_ids
         elif split == 'test':
+            subset_ids = test_ids
+        else:
+            subset_ids = None
+            raise ValueError(f'Split argument "{split}" is not recognized. \
+                             Please enter one of ["train", "validate", "test"]')
         
+        image_files = [
+            file for file in image_files
+            if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids)
+        ]
+            
+        mask_files = [
+            file for file in mask_files
+            if any(str(patient_id) in file.split('_')[0] for patient_id in subset_ids)
+        ]
+        assert len(image_files) == len(mask_files), "The number of image files\
+            does not match the number of mask files--verify subsetting operation."
+        
+        # Shuffle order of patient scans
+        # (note that only images need to be shuffled since masks and metadata
+        # will be linked to the selected image)
+        np.random.shuffle(image_files) 
+        
+        ## Generate next example
+        # ----------------------
+        for example in image_files:
+            
+            # Extract linking data
+            scan_id = example.replace('.mha', '')
+            patient_id = scan_id.split('_')[0]
+            scan_type = '_'.join(scan_id.split('_')[1:])
+
+            # Load .mha file
+            image_path = os.path.join(paths_dict['images'], 'images', example)
+            image = sitk.ReadImage(image_path)
+            
+            # Convert .mha image to numeric array
+            image_array = sitk.GetArrayFromImage(image)
             
+            # Extract overview data corresponding to image
+            
+            # Extract patient radiological gradings corresponding to image
+            
+            
+
+        
+
+
+
+
     def import_csv_data(filepath: str) -> List[Dict[str, str]]:
         """Import all rows of CSV file."""
         results = []