Datasets:

AnonRes
/

OpenMind

@@ -1,60 +1,63 @@
----
-license: cc-by-4.0
-task_categories:
-  - "image-feature-extraction"
-pretty_name: "The OpenMind Dataset"
-tags:
-  - 3d
-  - image
----
-# The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning
-![OpenMind Dataset](./assets/OpenMindDataset.png)
-## Description
-The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and provides 23 different MRI modalities/techniques from over 30 different scanners, representing a highly variable pre-training dataset.
-## Additional Features
-Aside from the 3D MRI Images, we provide stratified metadata for each of the 114k images, where made available in the original dataset in a unified format.
-Moreover, we provide a) anonymization masks, which delineate defaced regions, allowing to take them into account when developing reconstruction based pre-training methods and b) we provide anatomy masks, which delineate areas with anatomy, e.g. for cases where images were brain extracted. Similarly to the a) this allows to either ignore regions outside of this during reconstruction and allows sampling regions with anatomy, avoiding empty regions for contrastive learning approaches.
-## Dataset structure
-The dataset is structured akin to the original OpenNeuro datasets, following a modified BIDS format. Each dataset is stored in a separate folder, with each folder containing respective subjects, which contain their sessions and for each session the respective MRIs, as well as anonymization and anatomy masks. Instead of differentiating by imaging type, we just provide all images in a flat folder
-The common metadata is stored outside of the dataset folder in a separate metadata file.
-```
--- Readme.md    // this readme
--- openmind_dataset
-    |-- metadata.csv
-    |-- data
-        |-- ds_000001
-            |-- sub-01
-                |-- ses-01
-                    |-- sub-01_ses-01_T1w.nii.gz
-                    |-- sub-01_ses-01_T1w_anon_mask.nii.gz
-                    |-- sub-01_ses-01_T1w_anatomy_mask.nii.gz
-                    |-- sub-01_ses-01_T2w.nii.gz
-                    |-- sub-01_ses-01_T2w_anon_mask.nii.gz
-                    |-- sub-01_ses-01_T2w_anatomy_mask.nii.gz
-                ...
-            ...
-        ...
-        |-- ds_xxxxxx
-        ...
-```
-Available meta-data tags (Name [Percent of images with this metadata]) within the metadata.csv file are:
-- MR Modalitiy/Technique [100%]
-- Scanner Manufacturer [100%]
-- Scanner Model [100%]
-- Scanner Field Strength [100%]
-- Age [70%]
-- Sex [77.4%]
-- Weight [1.5%]
-- BMI [15.7%]
-- Race [11.7%]
-- Handedness (right/left/ambidexterous) [35.4%]
-- Health Status (healthy/ill) [26.4%]

+---
+license: cc-by-4.0
+task_categories:
+  - "image-feature-extraction"
+pretty_name: "The OpenMind Dataset"
+tags:
+  - 3d
+  - image
+---
+# The OpenMind Dataset: A large-scale Head-And-Neck 3D MRI Dataset for self-supervised learning
+![OpenMind Dataset](./assets/OpenMindDataset.png)
+## Description
+The OpenMind Dataset is a large-scale 3D MRI dataset of the head and neck region featuring 114k MRI Images. Its purpose is to provide access of large amounts of 3D medical imaging data to accelerate the development of self-supervised learning methods for 3D medical imaging. This data was pooled from exactly 800 datasets from the OpenNeuro platform and provides 23 different MRI modalities/techniques from over 30 different scanners, representing a highly variable pre-training dataset.
+## Additional Features
+Aside from the 3D MRI Images, we provide stratified metadata for each of the 114k images when made available by the original dataset in a unified format.
+Moreover, we provide a) `deface_masks`, which delineate anonymized/defaced regions, allowing to take them into account when developing reconstruction based pre-training methods and b) `anatomy_masks`, which delineate areas which holds anatomy, e.g. for cases where images were brain extracted. Similarly to the `deface_masks` this allows to either ignore regions outside of this during reconstruction and allows sampling regions with anatomy, avoiding empty regions for contrastive learning approaches.
+## Dataset structure
+The dataset is structured akin to the original OpenNeuro datasets, following a modified BIDS format, exemplified below.
+We recommend using the `openneuro_metadata.csv` which holds the relative paths from the root directory to the images and their associated masks as well as metadata.
+```
+-- Readme.md    // this readme
+-- openneuro_metadata.csv  // metadata file containing the relative paths to the images and their metadata
+-- openmind_dataset
+    |-- openneuro_metadata.csv     # Contains the relative paths to the images and their metadata
+    |-- OpenMind
+        |-- ds_000001
+            |-- sub-01
+                |-- anat
+                    |-- sub-01_ses-01_T1w.nii.gz  # 3D Image in Nifti format
+                    |-- sub-01_T1w__Data          # Associated Folder holding Masks to Image
+                        |-- deface_mask.nii.gz      # deface mask
+                        \-- fb_mask.nii.gz          # anatomy mask
+                    |-- sub-01_inplaneT2.nii.gz
+                    \-- sub-01_inplaneT2__Data   # Associated Folder holding Masks to Image
+                        |-- deface_mask.nii.gz
+                        \-- fb_mask.nii.gz
+                ...
+            ...
+        ...
+        |-- ds_xxxxxx
+        ...
+```
+Available meta-data tags (Name [Percent of images with this metadata]) within the metadata.csv file are:
+- MR Modalitiy/Technique [100%]
+- Scanner Manufacturer [100%]
+- Scanner Model [100%]
+- Scanner Field Strength [100%]
+- Age [70%]
+- Sex [77.4%]
+- Weight [1.5%]
+- BMI [15.7%]
+- Race [11.7%]
+- Handedness (right/left/ambidexterous) [35.4%]
+- Health Status (healthy/ill) [26.4%]