Dataset Restricted Access

TrackVes Dataset

Penza, Veronica; Du, Xiaofei; Stoyanov, Danail; Forgione, Antonello; Mattos, Leonardo; De Momi, Elena

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

 

Restricted Access

You may request access to the files in this upload, provided that you fulfil the conditions below. The decision whether to grant/deny access is solely under the responsibility of the record owner.


Only people interested in using this dataset for research development.


  • 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.

543
30
views
downloads
All versions This version
Views 543543
Downloads 3030
Data volume 51.6 GB51.6 GB
Unique views 443443
Unique downloads 2121

Share

Cite as