update README.md

README.md

@@ -11,18 +11,35 @@ tags:
 
 # Dataset Card for Spine Segmentation: Discs, Vertebrae and Spinal Canal (SPIDER)
 
-The SPIDER data set contains lumbar spine magnetic resonance images (MRI) and segmentation masks described in the following paper:
+The SPIDER dataset contains (human) lumbar spine magnetic resonance images (MRI) and segmentation masks described in the following paper:
 
-Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens, Matthieu Rutten, 
-Job L. C. van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, Nikolas Lessmann. (2023).
-*Lumbar spine segmentation in MR images: a dataset and a public benchmark.* https://arxiv.org/abs/2306.12217.
+- **van der Graaf, J.W., van Hooff, M.L., Buckens, C.F.M. et al. *Lumbar spine segmentation in MR images: a dataset and a public benchmark.*
+  Sci Data 11, 264 (2024). https://doi.org/10.1038/s41597-024-03090-w**
 
-The data were made publicly available through [Zenodo](https://zenodo.org/records/8009680), an open repository operated by CERN, and posted on 
-[Grand Challenge](https://spider.grand-challenge.org/).
+Original data are available on [Zenodo](https://zenodo.org/records/8009680). More information can be found at [SPIDER Grand Challenge](https://spider.grand-challenge.org/).
 
-(***Disclaimer**: I am not affiliated in any way with the aforementioned paper, researchers, or organizations. My only contribution is to curate the SPIDER data set 
-here on Hugging Face to increase accessibility. While I have taken care to curate the data in a way that maintains the integrity of the original data, any findings using this
-particular data set should be validated against the original data provided by the researchers on [Zenodo](https://zenodo.org/records/8009680).*)
+![Example MRI Image](docs/ex1.png)
+
+![Example MRI Image with Segmentation Mask](docs/ex2.png)
+
+## Getting Started
+
+First, you will need to install the following dependencies:
+
+* `datasets >= 2.18.0`
+* `scikit-image >= 0.19.3`
+* `SimpleITK >= 2.3.1`
+
+Then you can load the SPIDER dataset as follows:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("cdoswald/SPIDER, name="default", trust_remote_code=True)
+```
+
+More detailed examples for [loading](tutorials/load_data.ipynb) the dataset with different configurations 
+and using the dataset for [segmentation tasks](tutorials/segment_anything.ipynb) are provided in the [tutorials](tutorials) folder.
 
 ## Table of Contents
 
@@ -30,99 +47,133 @@ particular data set should be validated against the original data provided by th
 
 ## Dataset Description
 
-- **Paper:** [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://arxiv.org/abs/2306.12217)
+- **Paper:** [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://www.nature.com/articles/s41597-024-03090-w) 
 - **Repository:** [Zenodo](https://zenodo.org/records/8009680)
+- **Grand Challenge:** [SPIDER Grand Challenge](https://spider.grand-challenge.org/)
 
 ### Dataset Summary
 
 The dataset includes 447 sagittal T1 and T2 MRI series collected from 218 patients across four hospitals.
 Segmentation masks indicating the vertebrae, intervertebral discs (IVDs), and spinal canal are also included.
-Segmentation masks were created manually by a medical trainee under the supervision of
-a medical imaging expert and an experienced musculoskeletal radiologist.
+Segmentation masks were created manually by a medical trainee under the supervision of a medical imaging expert and an experienced musculoskeletal radiologist.
 
 In addition to MR images and segmentation masks, additional metadata (e.g., scanner manufacturer, pixel bandwidth, etc.), limited
 patient characteristics (biological sex and age, when available), and radiological gradings indicating specific degenerative
 changes can be loaded with the corresponding image data.
 
-## Dataset Structure
+### Modifications to Original Data
 
-### Data Instances
+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:
 
-There are 447 images and corresponding segmentation masks for 218 unique patients.
+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). 
+If you need a different standardization, you have two options:
 
-### Data Fields
-
-The following list includes the data fields available for importing:
-
-- Numeric representation of image
-  
-- Numeric representation of segmentation mask
-  - vertebrae
-  - intervertebral discs
-  - spinal canal
-    
-- Image characteristics
-  - number of vertebrae
-  - number of discs
-    
-- Patient characteristics
-  - biological sex
-  - age
-    
-- Scanner characteristics
-  - manufacturer
-  - manufacturer model name
-  - serial number
-  - software version
-  - echo numbers
-  - echo time
-  - echo train length
-  - flip angle
-  - imaged nucleus
-  - imaging frequency
-  - inplane phase encoding direction
-  - MR acquisition type
-  - magnetic field strength
-  - number of phase encoding steps
-  - percent phase field of view
-  - percent sampling
-  - photometric interpretation
-  - pixel bandwidth
-  - pixel spacing
-  - repetition time
-  - specific absorption rate (SAR)
-  - samples per pixel
-  - scanning sequence
-  - sequence name
-  - series description
-  - slice thickness
-  - spacing between slices
-  - specific character set
-  - transmit coil name
-  - window center
-  - window width
-
-(TODO: Will add variable descriptions after proposal approval)
+    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
+	
+	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)).
+
+2. Train, Validation, and Test Set: The original dataset contained 257 unique studies (i.e., patients) that were partitioned into 218 (85%) studies for the public training/validation set
+and 39 (15%) studies for the SPIDER Grand Challenge [hidden test set](https://spider.grand-challenge.org/data/). To enable users to train, validate, and test their models prior to submitting
+their models to the SPIDER Grand Challenge, the original 218 studies that comprised the public training/validation set were further partitioned using a 60%/20%/20% split. The original split
+for each study (i.e., training or validation set) is recorded in the `OrigSubset` variable in the study's linked metadata.
 
-### Data Splits
 
-The training set contains [x] images distributed as follows:
+## Dataset Structure
 
-- Unique individuals: [x]
+### Data Instances
 
-- Standard sagittal T1 images: [x]
-- Standard sagittal T2 images: [y]
-- Standard sagittal T2 SPACE images: [z]
-- 
-The validation set contains 87 images distributed as follows:
+There are 447 images and corresponding segmentation masks for 218 unique patients.
 
-- Unique individuals: [x]
+### Data Format/Fields
+
+The format for each generated data instance is as follows:
+
+1. **patient_id**: a unique ID number indicating the specific patient (note that many patients have more than one scan in the data)
+
+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
+
+4. **mask**: a 3-dimensional volumetric array (height, width, depth) of values indicating manually segmented feature of interest
+
+5. **image_path**: path to the local cache containing the original (non-rescaled and non-resized) MRI image
+
+6. **mask_path**: path to the local cache containing the original (non-rescaled and non-resized) segementation mask
+
+7. **metadata**: a dictionary of metadata of image, patient, and scanner characteristics:
+
+    - number of vertebrae
+    - number of discs
+    - biological sex
+    - age
+    - manufacturer
+    - manufacturer model name
+    - serial number
+    - software version
+    - echo numbers
+    - echo time
+    - echo train length
+    - flip angle
+    - imaged nucleus
+    - imaging frequency
+    - inplane phase encoding direction
+    - MR acquisition type
+    - magnetic field strength
+    - number of phase encoding steps
+    - percent phase field of view
+    - percent sampling
+    - photometric interpretation
+    - pixel bandwidth
+    - pixel spacing
+    - repetition time
+    - specific absorption rate (SAR)
+    - samples per pixel
+    - scanning sequence
+    - sequence name
+    - series description
+    - slice thickness
+    - spacing between slices
+    - specific character set
+    - transmit coil name
+    - window center
+    - window width
+
+9. **rad_gradings**: radiological gradings by an expert musculoskeletal radiologist indicating specific degenerative
+changes at all intervertebral disc (IVD) levels (see page 3 of the [original paper](https://www.nature.com/articles/s41597-024-03090-w)
+ for more details). The data are provided as a dictionary of lists; an element's position in the list indicates the IVD level. Some elements
+are ratings while others are binary indicators. For consistency, each list will have 10 elements, but some IVD levels may not be applicable
+to every image (which will be indicated with an empty string).
 
-- Standard sagittal T1 images: [x]
-- Standard sagittal T2 images: [y]
-- Standard sagittal T2 SPACE images: [z]
+### Data Splits
 
-An additional hidden test set (not available through Hugging Face) is available on the [SPIDER Grand Challenge](spider.grand-challenge.org).
+The dataset is split as follows:
+
+- Training set:
+	- 218 unique patients
+	- 304 images
+		- Standard sagittal T1: [x]
+		- Standard sagittal T2: [y]
+		- Standard sagittal T2 SPACE: [z]
+- Validation set:
+	- [X] unique patients
+	- 75 images
+		- Standard sagittal T1: [x]
+		- Standard sagittal T2: [y]
+		- Standard sagittal T2 SPACE: [z]
+- Test set:
+	- [X] unique patients
+	- 68 images
+		- Standard sagittal T1: [x]
+		- Standard sagittal T2: [y]
+		- Standard sagittal T2 SPACE: [z]
+
+An additional hidden test set provided by the paper authors
+(i.e., not available via HuggingFace) is available on the 
+[SPIDER Grand Challenge](https://spider.grand-challenge.org/spiders-challenge/).
 
 ## Image Resolution
 
@@ -130,27 +181,10 @@ Standard sagittal T1 and T2 image resolution ranges from 3.3 x 0.33 x 0.33 mm to
 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.
 [Source](https://spider.grand-challenge.org/data/)
 
-## Dataset Curation
-
-The data have been curated to enable users to load any of the following:
-
-- Raw image files
-- Raw segmentation masks
-- Numeric representations of images in tensor format
-- Numeric representations of segmentation masks in tensor format
-- Linked patient characteristics (limited to sex and age, if available)
-- Linked scanner characteristics
-  
-### Source Data
-
-### Processing Steps
-
-(Specifics to be determined, but will include:)
-
-1. Conversion of .mha files to numeric representations
-2. Linking of segmentation mask numeric representations to image files
-3. Linking of patient and scanner characteristics to image files
-4. Cleaning of patient and scanner characteristics
+Note that all images are rescaled to have a resolution in the range `[0, 255]` (i.e., unsigned 8-bit integers)
+for compatibility with HuggingFace `datasets` library. If you want to use the original resolution, you can
+load the original images from the local cache paths indicated in the `image_path` and `mask_path` features.
+See the data loading [tutorial](tutorials/load_data.ipynb) for more information.
 
 ## Additional Information
 
@@ -160,19 +194,9 @@ The dataset is published under a CC-BY 4.0 license: https://creativecommons.org/
 
 ### Citation
 
-Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens, Matthieu Rutten, 
-Job L. C. van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, Nikolas Lessmann. (2023).
-*Lumbar spine segmentation in MR images: a dataset and a public benchmark.* https://arxiv.org/abs/2306.12217.
-
-
-
-
-# Rescale mask intensities to [0, 255] and cast as UInt8 type
-mask = sitk.Cast(sitk.RescaleIntensity(sitk.ReadImage(mask_path)), sitk.sitkUInt8)
-# Rescale image intensities to [0, 255] and cast as UInt8 type
-image = sitk.Cast(sitk.RescaleIntensity(image), sitk.sitkUInt8)
+- van der Graaf, J.W., van Hooff, M.L., Buckens, C.F.M. et al. Lumbar spine segmentation in MR images: a dataset and a public benchmark. Sci Data 11, 264 (2024). https://doi.org/10.1038/s41597-024-03090-w.
 
-            # NOTE: since the original array shape is not standardized, cannot return in dataset
+### Disclaimer
 
-# Images and masks are resized to (512, 512, 30) and rescaled to [0, 255] (unisgned 8-bit integers); paths to original
-.mha images and masks are also provided if you would prefer to load original image (for example, using SimpleSITK library)
+I am not affiliated in any way with the aforementioned paper, researchers, or organizations. Please validate any findings using this curated dataset
+against the original data provided by the researchers on [Zenodo](https://zenodo.org/records/8009680).)