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- # Description
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  [paper](https://arxiv.org/abs/2012.12453) | [kaggle](https://www.kaggle.com/datasets/newslab/cholecseg8k)
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  The CholecSeg8k dataset, an extension of the Cholec80 collection, includes 8,080 carefully annotated images from laparoscopic cholecystectomy surgeries, selected from 17 video clips in Cholec80. Each image in CholecSeg8K is pixel-level annotated for thirteen different surgical elements. The dataset is efficiently organized in a directory structure, featuring 101 folders, each containing 80 frames at a resolution of 854x480, along with three types of masks for each frame: a color mask for visualization, an annotation tool mask, and a watershed mask for simplified processing. This comprehensive dataset, freely available under the CC BY-NC-SA 4.0 license, is a critical resource for advancing the field of computer-assisted surgical procedures.
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- # Loading the data
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  First install the `datasets` library, then run the following code,
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  ```python
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  ```
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- # Data card contact
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  Min Woo Sun (minwoos@stanford.edu)
 
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+ # Description:
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  [paper](https://arxiv.org/abs/2012.12453) | [kaggle](https://www.kaggle.com/datasets/newslab/cholecseg8k)
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  The CholecSeg8k dataset, an extension of the Cholec80 collection, includes 8,080 carefully annotated images from laparoscopic cholecystectomy surgeries, selected from 17 video clips in Cholec80. Each image in CholecSeg8K is pixel-level annotated for thirteen different surgical elements. The dataset is efficiently organized in a directory structure, featuring 101 folders, each containing 80 frames at a resolution of 854x480, along with three types of masks for each frame: a color mask for visualization, an annotation tool mask, and a watershed mask for simplified processing. This comprehensive dataset, freely available under the CC BY-NC-SA 4.0 license, is a critical resource for advancing the field of computer-assisted surgical procedures.
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+ # Loading the data:
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  First install the `datasets` library, then run the following code,
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  ```python
 
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  }
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  ```
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+ # Data card contact:
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  Min Woo Sun (minwoos@stanford.edu)