Dataset Restricted Access

EndoAbS Dataset

Veronica Penza


JSON Export

{
  "owners": [
    22579
  ], 
  "doi": "10.5281/zenodo.60593", 
  "stats": {
    "version_unique_downloads": 93.0, 
    "unique_views": 1185.0, 
    "views": 1589.0, 
    "version_views": 1570.0, 
    "unique_downloads": 93.0, 
    "version_unique_views": 1177.0, 
    "volume": 20497323576.0, 
    "version_downloads": 114.0, 
    "downloads": 114.0, 
    "version_volume": 20497323576.0
  }, 
  "links": {
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.640084.svg", 
    "doi": "https://doi.org/10.5281/zenodo.60593", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.640084", 
    "latest_html": "https://zenodo.org/record/60593", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.60593.svg", 
    "html": "https://zenodo.org/record/60593", 
    "latest": "https://zenodo.org/api/records/60593"
  }, 
  "conceptdoi": "10.5281/zenodo.640084", 
  "created": "2016-08-22T08:41:08+00:00", 
  "updated": "2020-01-24T19:26:20.365357+00:00", 
  "conceptrecid": "640084", 
  "revision": 15, 
  "id": 60593, 
  "metadata": {
    "access_right_category": "danger", 
    "doi": "10.5281/zenodo.60593", 
    "description": "<p>The <strong>EndoAbS Dataset</strong> (Endoscopic Abdominal Stereo Images Dataset) aims to provide to the computer assisted surgery community a dataset for the validation of 3D reconstruction algorithms.<br>\nIt is composed of:<br>\n- 120 pair of endoscopic stereo images of abdominal organs (liver, kidneys, spleen);<br>\n- corresponding ground truth in left-camera reference frame, generated using a laser scanner;<br>\n- camera calibration parameters;</p>\n\n<p>The images were captured under different conditions:<br>\n- different light levels;<br>\n- presence of smoke; &nbsp;<br>\n- two phantom-endoscope distances (~5cm or ~10cm);<br>\n&nbsp;<br>\nIf you use this dataset, please cite:<br>\n&nbsp;<br>\n&nbsp;Penza, V., Ciullo, A. S., Moccia, S., Mattos, L. S., &amp; De Momi, E. (2018). EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms. <em>The International Journal of Medical Robotics and Computer Assisted Surgery</em>, e1926.</p>\n\n<p>For further information, please contact veronica.penza@iit.it</p>", 
    "title": "EndoAbS Dataset", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "640084"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "60593"
          }
        }
      ]
    }, 
    "access_conditions": "<p>Only people interested in using this dataset for research development.</p>", 
    "references": [
      "A.S. Ciullo, V. Penza, L. Mattos, E. De Momi (2016)", 
      "\"Development of a surgical stereo endoscopic image dataset for", 
      "validating 3D stereo reconstruction algorithms.\" 6th Joint Workshop on", 
      "New Technologies for Computer/Robot Assisted Surgery.", 
      "Penza, V., Ortiz, J., Mattos, L. S., Forgione, A., & De Momi, E. (2016).", 
      "\"Dense soft tissue 3D reconstruction refined with super-pixel segmentation for", 
      "robotic abdominal surgery.\"  International journal of computer assisted radiology", 
      "and surgery, 11(2), 197-206."
    ], 
    "keywords": [
      "endoscopic stereo image dataset", 
      "3D reconstruction algorithm evaluation", 
      "ground truth", 
      "abdominal phantom model"
    ], 
    "publication_date": "2016-08-22", 
    "creators": [
      {
        "affiliation": "Politecnico di Milano", 
        "name": "Veronica Penza"
      }
    ], 
    "access_right": "restricted", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.640084", 
        "relation": "isVersionOf"
      }
    ]
  }
}
1,570
114
views
downloads
All versions This version
Views 1,5701,589
Downloads 114114
Data volume 20.5 GB20.5 GB
Unique views 1,1771,185
Unique downloads 9393

Share

Cite as