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diff --git a/README.md b/README.md
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+---
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- medical
+- blood-vessel
+- octa
+pretty_name: (Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
+size_categories:
+- 1K<n<10K
+---
+
+# syn-cerebral-octa-seg
+<div style="text-align: center;">
+  <img src="docs/synthetic3d.jpg" style="width: 70%; height: auto;">
+</div>
+
+## Introduction
+
+To accurately segment blood vessels in cerebral 3D Optical Coherence Tomography Angiography (OCTA) images, a vast amount of voxel-level annotations are required. However, the curation of manual annotations is a cumbersome and time-consuming task. To alleviate the need for manual annotation, we provide realistic synthetic data generated via our proposed synthesis pipeline.
+
+Our proposed synthesis pipeline is described in detail in our manuscript ([Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images](https://arxiv.org/abs/2403.07116)). Corresponding code and additional information can be found on [GitHub](https://github.com/bwittmann/syn-cerebral-octa-seg).
+
+**TL;DR:** *First*, we selectively extract patches from vessel graphs that match the FOV and morphological characteristics of vasculature contained in cerebral OCTA images and transform them into voxelized volumes; *second*, we transform the voxelized volumes into synthetic cerebral 3D OCTA images by simulating the most dominant image acquisition artifacts; and *third*, we use our synthetic cerebral 3D OCTA images paired with their matching ground truth labels to train a blood vessel segmentation network.
+
+## Dataset Summary
+The voxel size of all provided images is isotropic and corresponds to 2 μm.
+
+- [synthetic cerebral 3D OCTA images with metadata & matching ground truth labels of shape 250 x 250 x 250; full data available upon request.](https://huggingface.co/datasets/bwittmann/syn-cerebral-octa-seg/tree/main/synthetic_cerebral_octa)
+```
+synthetic_cerebral_octa/
+└── sample_0/
+    └── sim/
+        └── sim_data_xx.npy     # synthetic cerebral 3D OCTA image
+        └── sim_seg_xx.npy      # ground truth
+    └── ang.npy                 # metadata angle
+    └── occ.npy                 # metadata occupancy below
+    └── rad.npy                 # metadata radius
+    └── seg.npy                 # voxelized volume
+└── sample_1/
+    ...   
+```
+
+
+- [6 manually annotated OCTA volumes of shape shape 160 x 160 x 160.](https://huggingface.co/datasets/bwittmann/syn-cerebral-octa-seg/tree/main/manual_annotations)
+```
+manual_annotations/
+└── mx_0.nii                    # real cerebral 3D OCTA image
+└── mx_0_label.nii              # ground truth (manual annotations) 
+    ...
+```
+
+
+## Citation
+
+If you find our data useful for your research, please consider citing:
+```bibtex
+@inproceedings{wittmann2024simulation,
+  title={Simulation-based segmentation of blood vessels in cerebral 3D OCTA images},
+  author={Wittmann, Bastian and Glandorf, Lukas and Paetzold, Johannes C and Amiranashvili, Tamaz and W{\"a}lchli, Thomas and Razansky, Daniel and Menze, Bjoern},
+  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
+  pages={645--655},
+  year={2024},
+  organization={Springer}
+}
+```
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