cleaned up comments

SPIDER.py

@@ -11,20 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
+"""TODO: Add a description here.""" #TODO
 
 # Import packages
 import csv
-import json
 import os
-from typing import Dict, List, Mapping, Optional, Set, Sequence, Tuple, Union
+from typing import Dict, List, Mapping, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import pandas as pd
 
 import datasets
-import PIL
 import skimage
 import SimpleITK as sitk
 
@@ -59,14 +56,17 @@ MAX_IVD = 9
 DEFAULT_SCAN_TYPES = ['t1', 't2', 't2_SPACE']
 DEFAULT_RESIZE = (512, 512, 30)
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
+@misc{vandergraaf2023lumbar,
+      title={Lumbar spine segmentation in MR images: a dataset and a public benchmark}, 
+      author={Jasper W. van der Graaf and Miranda L. van Hooff and \
+              Constantinus F. M. Buckens and Matthieu Rutten and \
+              Job L. C. van Susante and Robert Jan Kroeze and \
+              Marinus de Kleuver and Bram van Ginneken and Nikolas Lessmann},
+      year={2023},
+      eprint={2306.12217},
+      archivePrefix={arXiv},
+      primaryClass={eess.IV}
 }
 """
 
@@ -81,9 +81,6 @@ _HOMEPAGE = "https://zenodo.org/records/10159290"
 _LICENSE = """Creative Commons Attribution 4.0 International License \
 (https://creativecommons.org/licenses/by/4.0/legalcode)"""
 
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
     "images":"https://zenodo.org/records/10159290/files/images.zip",
     "masks":"https://zenodo.org/records/10159290/files/masks.zip",
@@ -111,45 +108,11 @@ class CustomBuilderConfig(datasets.BuilderConfig):
 class SPIDER(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
+    # Class attributes
     DEFAULT_WRITER_BATCH_SIZE = 16 # PyArrow default is too large for image data
-
     VERSION = datasets.Version("1.1.0")
-
     BUILDER_CONFIG_CLASS = CustomBuilderConfig
 
-    # BUILDER_CONFIGS = [
-    #     CustomBuilderConfig(
-    #         name="all_scan_types",
-    #         version=VERSION,
-    #         description="Use images of all scan types (t1, t2, t2 SPACE)",
-    #         scan_types=['t1', 't2', 't2_SPACE'],
-    #         resize_shape=DEFAULT_RESIZE,
-    #     ),
-    #     CustomBuilderConfig(
-    #         name="t1_scan_types",
-    #         version=VERSION,
-    #         description="Use images of t1 scan types only",
-    #         scan_types=['t1'],
-    #         resize_shape=DEFAULT_RESIZE,
-    #     ),
-    #     CustomBuilderConfig(
-    #         name="t2_scan_types",
-    #         version=VERSION,
-    #         description="Use images of t2 scan types only",
-    #         scan_types=['t2'],
-    #         resize_shape=DEFAULT_RESIZE,
-    #     ),
-    #     CustomBuilderConfig(
-    #         name="t2_SPACE_scan_types",
-    #         version=VERSION,
-    #         description="Use images of t2 SPACE scan types only",
-    #         scan_types=['t2_SPACE'],
-    #         resize_shape=DEFAULT_RESIZE,
-    #     ),
-    # ]
-
-    # DEFAULT_CONFIG_NAME = "all_scan_types"
-
     def __init__(
         self,
         *args,
@@ -162,10 +125,9 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         self.resize_shape = resize_shape
 
     def _info(self):
-        """
-        This method specifies the datasets.DatasetInfo object which contains 
-        informations and typings for the dataset.
-        """
+        """Specify datasets.DatasetInfo object containing information and typing
+        for the dataset."""
+
         image_size = self.config.resize_shape
         features = datasets.Features({
             "patient_id": datasets.Value("string"),
@@ -227,32 +189,16 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         })
 
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
+            features=features,
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        """
-        This method is tasked with downloading/extracting the data 
-        and defining the splits depending on the configuration
-        If several configurations are possible (listed in BUILDER_CONFIGS), 
-        the configuration selected by the user is in self.config.name
-        """
+        """Download and extract data and define splits based on configuration."""
 
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         paths_dict = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
@@ -373,13 +319,6 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             overview_dict[key] = {
                 k:v for k,v in item.items() if k not in exclude_vars
             }
-        
-        # # Determine maximum number of radiological gradings per patient
-        # max_ivd = 0
-        # for temp_dict_1 in grades_dict.values():
-        #     for temp_dict_2 in temp_dict_1:
-        #         if int(temp_dict_2['IVD label']) > max_ivd:
-        #             max_ivd = int(temp_dict_2['IVD label'])
 
         # Merge patient records for radiological gradings data
         grades_dict = {}
@@ -478,10 +417,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             # Load .mha image file
             image_path = os.path.join(paths_dict['images'], 'images', example)
             image = sitk.ReadImage(image_path)
-            
-            # # Rescale image intensities to [0, 255] and cast as UInt8 type
-            # image = sitk.Cast(sitk.RescaleIntensity(image), sitk.sitkUInt8)
-        
+
             # Convert .mha image to original size numeric array
             image_array_original = sitk.GetArrayFromImage(image)
             
@@ -491,14 +427,9 @@ class SPIDER(datasets.GeneratorBasedBuilder):
                 resize_shape,
             )
 
-            # NOTE: since the original array shape is not standardized, cannot return in dataset
-            
             # Load .mha mask file
             mask_path = os.path.join(paths_dict['masks'], 'masks', example)
             mask = sitk.ReadImage(mask_path)
-            
-            # # Rescale mask intensities to [0, 255] and cast as UInt8 type
-            # mask = sitk.Cast(sitk.RescaleIntensity(mask), sitk.sitkUInt8)
 
             # Convert .mha mask to original size numeric array
             mask_array_original = sitk.GetArrayFromImage(mask)