Repository: anjany/verse
Branch: main
Commit: 02b292b86021
Files: 9
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Directory structure:
gitextract_gbp5qvot/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── utils/
├── data_utilities.py
├── eval_utilities.py
├── evaluate.ipynb
├── prepare_data.ipynb
└── sample/
└── sub-verse004_seg-subreg_ctd.json
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FILE CONTENTS
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FILE: .gitignore
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# Byte-compiled / optimized / DLL files
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.DS*
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FILE: LICENSE
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MIT License
Copyright (c) 2020 Anjany Kumar Sekuboyina
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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FILE: README.md
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# VerSe: Large Scale Vertebrae Segmentation Challenge
_Look well to the spine for the cause of disease_ - Hippocrates
*VerSe examples. Observe the variability in data: field-of-view, fractures, transitional vertebrae, etc.*
Table of Contents
* [What is VerSe?](#what-is-verse)
* [Citing VerSe](#citing-verse)
* [Data](#data)
* [Download](#download)
* [License](#license)
* [Code](#code)
* [Contact](#contact)
* [Other Related Work](#other-related-work)
* [Acknowledgements](#acknowledgements)
## What is VerSe?
Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable.
We believe *VerSe* can help here. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation.
## Citing VerSe
If you use VerSe, we would appreciate references to the following papers.
1. **Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.**"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# get vocel data\n",
"im_np = img_iso.get_fdata()\n",
"msk_np = msk_iso.get_fdata()\n",
"\n",
"\n",
"# get the mid-slice of the scan and mask in both sagittal and coronal planes\n",
"\n",
"im_np_sag = im_np[:,:,int(im_np.shape[2]/2)]\n",
"im_np_cor = im_np[:,int(im_np.shape[1]/2),:]\n",
"\n",
"msk_np_sag = msk_np[:,:,int(msk_np.shape[2]/2)]\n",
"msk_np_sag[msk_np_sag==0] = np.nan\n",
"\n",
"msk_np_cor = msk_np[:,int(msk_np.shape[1]/2),:]\n",
"msk_np_cor[msk_np_cor==0] = np.nan\n",
"\n",
"# plot \n",
"fig, axs = create_figure(96,im_np_sag, im_np_cor)\n",
"\n",
"axs[0].imshow(im_np_sag, cmap=plt.cm.gray, norm=wdw_sbone)\n",
"axs[0].imshow(msk_np_sag, cmap=cm_itk, alpha=0.3, vmin=1, vmax=64)\n",
"plot_sag_centroids(axs[0], ctd_iso, zooms)\n",
"\n",
"axs[1].imshow(im_np_cor, cmap=plt.cm.gray, norm=wdw_sbone)\n",
"axs[1].imshow(msk_np_cor, cmap=cm_itk, alpha=0.3, vmin=1, vmax=64)\n",
"plot_cor_centroids(axs[1], ctd_iso, zooms)"
]
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FILE: utils/sample/sub-verse004_seg-subreg_ctd.json
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[{"direction": ["P", "I", "R"]}, {"label": 16, "X": 79.1, "Y": 40.9, "Z": 32.3}, {"label": 17, "X": 79.3, "Y": 63.4, "Z": 32.2}, {"label": 18, "X": 76.0, "Y": 88.6, "Z": 32.1}, {"label": 19, "X": 70.6, "Y": 117.3, "Z": 31.6}, {"label": 20, "X": 63.2, "Y": 141.9, "Z": 31.8}, {"label": 21, "X": 53.0, "Y": 167.7, "Z": 30.8}, {"label": 22, "X": 46.5, "Y": 202.0, "Z": 29.8}, {"label": 23, "X": 46.8, "Y": 235.3, "Z": 29.1}, {"label": 24, "X": 57.2, "Y": 266.4, "Z": 29.3}]