Dataset


The EndoAbS Dataset

The EndoAbS Dataset (Endoscopic Abdominal Stereo Images Dataset) aims to provide the computer assisted surgery community with a dataset for the validation of 3D reconstruction algorithms.

It is composed of:

  • 120 pairs of endoscopic stereo images of abdominal organs (liver, kidney, spleen);
  • corresponding ground truth in left­camera reference frame, generated using a laser scanner;
  • camera calibration parameters;

The images were captured under different conditions:

  • 3 different light levels;
  • presence of smoke;
  • two phantom­endoscope distances (~5cm or ~10cm);

To download the dataset, please goto: https://zenodo.org/record/60593

If you use this dataset, please cite:

A.S. Ciullo, V. Penza, L. Mattos, E. De Momi
“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. 2016.

We will soon upload the more detailed Journal paper!

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

VeronicaThesisImage1


The TrackVes dataset

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

It is composed of:

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

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_generatorapp/tracking_evaluation.

To download the dataset, please goto: https://zenodo.org/record/822053

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