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Published July 2, 2017 | Version v1
Dataset Restricted

TrackVes Dataset

  • 1. Politecnico di Milano, Istituto Italiano di Tecnologia
  • 2. University College London
  • 3. Ospedale Niguarda Ca' Granda, Milano - AIMS Academy
  • 4. Istituto Italiano di Tecnologia
  • 5. Politecnico di Milano

Description

The TrackVes dataset provides to the computer assisted surgery community a dataset for the validation of soft tissue tracking algorithms.

It is composed of nine folders:

EV1:3          - 3 video sequences of ex-vivo organs (kidney and liver);
IV1:6           - 6 video sequences of in-vivo organs (real abdominal surgical scenes);

Each folder contains:

videoLeft.avi    - video sequence;
GT.xml             - xml file with ground truth defined as a 2D polygon around the area to be tracked, with an interframe step of 10;
Attributes.xml  - xml file indicating one of the following attributes associated to each frame (with an interframe step of 10):

      0 - Safety Area Visible (SAV);
      1 - Partial Occlusion (PO);
      2 - Safety Area Out of Field of View (OFV);
      3 - Total Occlusion (TO);
      4 - DEFormation (DEF);
      5 - Illumination Changes (IC);
      6 - Abrupt Camera Motion (ACM);
      7 - Blur (BLR);
      8 - Presence of Smoke (SMK).


The C++ code implementing the ground truth generation and the tracking algorithm evaluation is available on Bitbucket -
https://bitbucket.org/ververo/envisors_track/ as:

app/gt_generator
app/tracking_evaluation.

If you use this dataset, please cite:

V. Penza, X. Du, D. Stoyanov, A. Forgione, L. Mattos and E. De Momi. "Long Term Safety Area Tracking (LT-SAT) with Online Failure Detection and Recovery for Robotic Minimally Invasive Surgery", Medical Image Analysis, 2017.

For further information, please contact veronica.penza@iit.it

 

Files

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Additional details

References

  • V. Penza, X. Du, D. Stoyanov, A. Forgione, L. Mattos and E. De Momi. "Long Term Safety Area Tracking (LT-SAT) with Online Failure Detection and Recovery for Robotic Minimally Invasive Surgery", Medical Image Analysis, 2017.