updated README

README.md

@@ -113,12 +113,14 @@ changes can be loaded with the corresponding image data.
 This version of the SPIDER dataset (i.e., available through the HuggingFace `datasets` library) differs from the original 
 data available on [Zenodo](https://zenodo.org/records/8009680) in two key ways:
 
-1. Image Rescaling/Resizing: The original 3D volumetric MRI data (images and masks) are stored as .mha files and do not have a standardized height, width, depth, and image resolution. 
-To enable the data to be loaded through the HuggingFace `datasets` library, all 447 MRI series and masks are standardized to have size `(512, 512, 30)` and resolution `[0, 255]` (unisgned 8-bit integers); therefore,
-n-dimensional interpolation is used to resize and/or rescale the images (via the `skimage.transform.resize` and `skimage.img_as_ubyte` functions). 
+1. Image Rescaling/Resizing: The original 3D volumetric MRI data are stored as .mha files and do not have a standardized height, width, depth, and image resolution. 
+To enable the data to be loaded through the HuggingFace `datasets` library, all 447 MRI series are standardized to have height and width of `(512, 512)` and (unsigned) 16-bit integer resolution.
+Segmentation masks have the same height and width dimension but are (unsigned) 8-bit integer resolution.
+The depth dimension has not been modified; rather, each scan is formatted as a sequence of `(512, 512)` grayscale images, where the index in the sequence indicates the depth value.
+N-dimensional interpolation is used to resize and/or rescale the images (via the `skimage.transform.resize` and `skimage.img_as_uint` functions). 
 If you need a different standardization, you have two options:
 
-    i. Pass your preferred standardization size as a `Tuple[int, int, int]` to the `resize_shape` argument in `load_dataset` (see the [LoadData Tutorial](placeholder)); OR
+    i. Pass your preferred height and width size as a `Tuple[int, int]` to the `resize_shape` argument in `load_dataset` (see the [LoadData Tutorial](placeholder)); OR
 	
 	ii. After loading the dataset from HuggingFace, use the `SimpleITK` library to import each image using the file path of the locally cached .mha file. 
 	The local cache file path is provided for each example when iterating over the dataset (again, see the [LoadData Tutorial](placeholder)).
@@ -142,9 +144,9 @@ The format for each generated data instance is as follows:
 
 2. **scan_type**: an indicator for whether the image is a T1-weighted, T2-weighted, or T2-SPACE MRI
 
-3. **image**: a 3-dimensional volumetric array (height, width, depth) of values indicating pixel intensities of MRI scan
+3. **image**: a sequence of 2-dimensional grayscale images of the MRI scan
 
-4. **mask**: a 3-dimensional volumetric array (height, width, depth) of values indicating the following segmented anatomical feature(s):
+4. **mask**: a sequence of 2-dimensional values indicating the following segmented anatomical feature(s):
 
     - 0 = background
     - 1-25 = vertebrae (numbered from the bottom, i.e., L5 = 1)
@@ -235,7 +237,7 @@ An additional hidden test set provided by the paper authors
 > Sagittal T2 SPACE sequence images had a near isotropic spatial resolution with a voxel size of 0.90 x 0.47 x 0.47 mm.
 > (https://spider.grand-challenge.org/data/)
 
-Note that all images are rescaled to have pixel intensities in the range `[0, 255]` (i.e., unsigned 8-bit integers)
+Note that all images are rescaled to have unsigned 16-bit integer resolution
 for compatibility with the HuggingFace `datasets` library. If you want to use the original resolution, you can
 load the original images from the local cache indicated in each example's `image_path` and `mask_path` features.
 See the [tutorial](tutorials/load_data.ipynb) for more information.
@@ -257,6 +259,6 @@ against the original data provided by the researchers on [Zenodo](https://zenodo
 
 ### Known Issues/Bugs
 
-1. Serializing data into Apache Arrow format is required to make the dataset available via HuggingFace's `datasets` library. However, it introduces some segmentation
+1. Serializing data into Apache Arrow format is required to make the dataset available via HuggingFace's `datasets` library. However, it can introduce some segmentation
 mask integer values that do not map exactly to a defined [anatomical feature category](https://grand-challenge.org/algorithms/spider-baseline-iis/).
 See the data loading [tutorial](tutorials/load_data.ipynb) for more information and temporary work-arounds.