2 datasets found
  1. P

    Endomapper Dataset

    • paperswithcode.com
    Updated Apr 28, 2022
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    Pablo Azagra; Carlos Sostres; Ángel Ferrandez; Luis Riazuelo; Clara Tomasini; Oscar León Barbed; Javier Morlana; David Recasens; Victor M. Batlle; Juan J. Gómez-Rodríguez; Richard Elvira; Julia López; Cristina Oriol; Javier Civera; Juan D. Tardós; Ana Cristina Murillo; Angel Lanas; José M. M. Montiel (2022). Endomapper Dataset [Dataset]. https://paperswithcode.com/dataset/endomapper
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    Dataset updated
    Apr 28, 2022
    Authors
    Pablo Azagra; Carlos Sostres; Ángel Ferrandez; Luis Riazuelo; Clara Tomasini; Oscar León Barbed; Javier Morlana; David Recasens; Victor M. Batlle; Juan J. Gómez-Rodríguez; Richard Elvira; Julia López; Cristina Oriol; Javier Civera; Juan D. Tardós; Ana Cristina Murillo; Angel Lanas; José M. M. Montiel
    Description

    The Endomapper dataset is the first collection of complete endoscopy sequences acquired during regular medical practice, including slow and careful screening explorations, making secondary use of medical data. Its original purpose is to facilitate the development and evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in real endoscopy data. The first release of the dataset is composed of 50 sequences with a total of more than 13 hours of video. It is also the first endoscopic dataset that includes both the computed geometric and photometric endoscope calibration as well as the original calibration videos. Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with groundtruth and meta-data related to special cases, such as sequences from the same patient. This information will improve the research in endoscopic VSLAM, as well as other research lines, and create new research lines.

  2. u

    Data from: Simcol3D - 3D Reconstruction during Colonoscopy Challenge Dataset...

    • rdr.ucl.ac.uk
    bin
    Updated Sep 7, 2023
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    Anita Rau; Sophia Bano; Yueming Jin; Danail Stoyanov (2023). Simcol3D - 3D Reconstruction during Colonoscopy Challenge Dataset [Dataset]. http://doi.org/10.5522/04/24077763.v1
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    binAvailable download formats
    Dataset updated
    Sep 7, 2023
    Dataset provided by
    University College London
    Authors
    Anita Rau; Sophia Bano; Yueming Jin; Danail Stoyanov
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Colorectal cancer is one of the most common cancers in the world. By establishing a benchmark, SimCol3D aimed to facilitate data-driven navigation during colonoscopy. More details about the challenge and corresponding data can be found in the challenge paper on arXiv.

    The challenge consisted of simulated colonoscopy data and images from real patients. This data release encompasses the synthetic portion of the challenge. The synthetic data includes three different anatomies derived from real human CT scans. Each anatomy provides several randomly generated trajectories with RGB renderings, camera intrinsics, ground truth depths, and ground truth poses. In total, this dataset includes more than 37,000 labelled images.

    The real colonoscopy data used in the SimCol3D challenge consists of images extracted from the EndoMapper dataset. The real data is available on the EndoMapper Synapse page.

    The synthetic colonoscopy data is made available in this repository.

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Share
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TwitterTwitter
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Click to copy link
Link copied
Close
Cite
Pablo Azagra; Carlos Sostres; Ángel Ferrandez; Luis Riazuelo; Clara Tomasini; Oscar León Barbed; Javier Morlana; David Recasens; Victor M. Batlle; Juan J. Gómez-Rodríguez; Richard Elvira; Julia López; Cristina Oriol; Javier Civera; Juan D. Tardós; Ana Cristina Murillo; Angel Lanas; José M. M. Montiel (2022). Endomapper Dataset [Dataset]. https://paperswithcode.com/dataset/endomapper

Endomapper Dataset

Explore at:
87 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 28, 2022
Authors
Pablo Azagra; Carlos Sostres; Ángel Ferrandez; Luis Riazuelo; Clara Tomasini; Oscar León Barbed; Javier Morlana; David Recasens; Victor M. Batlle; Juan J. Gómez-Rodríguez; Richard Elvira; Julia López; Cristina Oriol; Javier Civera; Juan D. Tardós; Ana Cristina Murillo; Angel Lanas; José M. M. Montiel
Description

The Endomapper dataset is the first collection of complete endoscopy sequences acquired during regular medical practice, including slow and careful screening explorations, making secondary use of medical data. Its original purpose is to facilitate the development and evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in real endoscopy data. The first release of the dataset is composed of 50 sequences with a total of more than 13 hours of video. It is also the first endoscopic dataset that includes both the computed geometric and photometric endoscope calibration as well as the original calibration videos. Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with groundtruth and meta-data related to special cases, such as sequences from the same patient. This information will improve the research in endoscopic VSLAM, as well as other research lines, and create new research lines.

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