added mask array

SPIDER.py

@@ -168,9 +168,9 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             "patient_id": datasets.Value("string"),
             "scan_type": datasets.Value("string"),
             # "raw_image": datasets.Image(),
-            "image_array": datasets.Array3D(shape=image_size, dtype='float32'),
+            "image_array": datasets.Array3D(shape=image_size, dtype='float64'),
             # "raw_mask": datasets.Image(),
-            "mask_array": datasets.Array3D(shape=image_size, dtype='float32'),
+            "mask_array": datasets.Array3D(shape=image_size, dtype='float64'),
             "metadata": {
                 "num_vertebrae": datasets.Value(dtype="string"), #TODO: more specific types
                 "num_discs": datasets.Value(dtype="string"),
@@ -289,10 +289,6 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         resize_shape: Tuple[int, int, int],
         validate_share: float = 0.3,
         test_share: float = 0.2,
-        raw_image: bool = True,
-        numeric_array: bool = True,
-        metadata: bool = True,
-        rad_gradings: bool = True,
         random_seed: int = 9999,
     ) -> Tuple[str, Dict]:
         """
@@ -312,13 +308,6 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             test_share: float indicating share of data to use for testing;
                 must be in range (0.0, 1.0); note that training share is 
                 calculated as (1 - validate_share - test_share)
-            raw_image: indicates whether to include .mha image file in example
-            numeric_array: indicates whether to include numpy numeric array of
-                image in example
-            metadata: indicates whether to include patient and scanner metadata
-                with image example
-            rad_gradings: indicates whether to include patient's radiological
-                gradings with image example
         
         Yields
             Tuple (unique patient-scan ID, dict of 
@@ -483,7 +472,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             patient_id = scan_id.split('_')[0]
             scan_type = '_'.join(scan_id.split('_')[1:])
 
-            # Load .mha file
+            # Load .mha image file
             image_path = os.path.join(paths_dict['images'], 'images', example)
             image = sitk.ReadImage(image_path)
             
@@ -492,8 +481,15 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 sitk.GetArrayFromImage(image), resize_shape
             )
 
-            #TODO: load mask file and numeric array
+            # Load .mha mask file
+            mask_path = os.path.join(paths_dict['masks'], 'masks', example)
+            mask = sitk.ReadImage(mask_path)
             
+            # Convert .mha mask to standardized numeric array
+            mask_array = standardize_3D_image(
+                sitk.GetArrayFromImage(mask), resize_shape
+            )
+    
             # Extract overview data corresponding to image
             image_overview = overview_dict[scan_id]
 
@@ -507,7 +503,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 'raw_image':None, #TODO
                 'raw_mask':None, #TODO
                 'image_array':image_array,
-                'mask_array':None, #TODO
+                'mask_array':mask_array,
                 'metadata':image_overview,
                 'rad_gradings':patient_grades_dict,
                 }