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bwittmann
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syn-cerebral-octa-seg

Tasks:
Image Segmentation
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
medical
blood-vessel
octa
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  1. .gitattributes +67 -0
  2. README.md +68 -0
  3. docs/synthetic3d.jpg +3 -0
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  22. synthetic_cerebral_octa/sample_10/ang.npy +3 -0
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  25. synthetic_cerebral_octa/sample_10/seg.npy +3 -0
  26. synthetic_cerebral_octa/sample_10/sim/sim_data_b421.npy +3 -0
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  28. synthetic_cerebral_octa/sample_11/ang.npy +3 -0
  29. synthetic_cerebral_octa/sample_11/occ.npy +3 -0
  30. synthetic_cerebral_octa/sample_11/rad.npy +3 -0
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  47. synthetic_cerebral_octa/sample_14/occ.npy +3 -0
  48. synthetic_cerebral_octa/sample_14/rad.npy +3 -0
  49. synthetic_cerebral_octa/sample_14/seg.npy +3 -0
  50. synthetic_cerebral_octa/sample_14/sim/sim_data_b421.npy +3 -0
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README.md ADDED
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+ ---
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+ task_categories:
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+ - image-segmentation
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+ language:
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+ - en
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+ tags:
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+ - medical
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+ - blood-vessel
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+ - octa
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+ pretty_name: (Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+
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+ # syn-cerebral-octa-seg
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+ <div style="text-align: center;">
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+ <img src="docs/synthetic3d.jpg" style="width: 70%; height: auto;">
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+ </div>
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+
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+ ## Introduction
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+
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+ 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.
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+
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+ 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).
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+
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+ **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.
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+
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+ ## Dataset Summary
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+ The voxel size of all provided images is isotropic and corresponds to 2 μm.
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+
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+ - [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)
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+ ```
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+ synthetic_cerebral_octa/
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+ └── sample_0/
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+ └── sim/
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+ └── sim_data_xx.npy # synthetic cerebral 3D OCTA image
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+ └── sim_seg_xx.npy # ground truth
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+ └── ang.npy # metadata angle
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+ └── occ.npy # metadata occupancy below
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+ └── rad.npy # metadata radius
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+ └── seg.npy # voxelized volume
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+ └── sample_1/
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+ ...
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+ ```
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+
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+
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+ - [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)
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+ ```
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+ manual_annotations/
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+ └── mx_0.nii # real cerebral 3D OCTA image
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+ └── mx_0_label.nii # ground truth (manual annotations)
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+ ...
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+ ```
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+
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+
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+ ## Citation
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+
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+ If you find our data useful for your research, please consider citing:
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+ ```bibtex
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+ @inproceedings{wittmann2024simulation,
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+ title={Simulation-based segmentation of blood vessels in cerebral 3D OCTA images},
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+ author={Wittmann, Bastian and Glandorf, Lukas and Paetzold, Johannes C and Amiranashvili, Tamaz and W{\"a}lchli, Thomas and Razansky, Daniel and Menze, Bjoern},
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+ booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
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+ pages={645--655},
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+ year={2024},
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+ organization={Springer}
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+ }
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+ ```
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