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Hamlyn Centre Laparoscopic / Endoscopic Video Datasets

Welcome to the Hamlyn Centre Laparoscopic / Endoscopic Video Dataset Page. Image processing and computer vision research can be carried out using cheap webcams. However, working with medical data requires access to hospitals, patient consent and validation (requiring expensive hardware). This webpage has been created to provide easy access to in vivo patient datasets and validation datasets. Permission is given to use and publish all data on this website. If you use these datasets we request that you cite the appropriate paper(s).

We are in the process of adding more data and this webpage will be periodically updated with new datasets.

Implementation of the Affine-invariant anisotropic feature detector
The implementation of the Affine-invariant anisotropic feature detector can be downloaded here.
If you use this code please reference paper [6]. If you have any questions about the executable please contact stamatia.giannarou03 "at" imperial.ac.uk

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2.5Gb

NEW! A video dataset that contains ten in vivo sequences and ground truth data for region tracking and retargeting in GI endoscopy. These videos were collected in standard GI examinations, and they involve challenges in endoscopy, such as tissue deformation and rapid endoscope movement. [7]

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In vivo porcine procedure of diaphragm dissection. The data involves blur due to cauterisation induced smoke, significant deformation due to cardiac motion, artefacts due to bleeding, specular reflections and instrument occlusion. [6]
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141.1Mb

In vivo TECAB procedure. The data involves soft tissue deformation and illumination changes. [6]
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1.0Gb

In vivo lung lobectomy procedure. The data involves significant deformation due to respiration and instrument tissue interaction. [6]
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1.3Gb

Low quality in vivo data captured using a Medigus Camera mounted on an articulated laparoscopic robot during an intra-abdominal exploration. The sequence involves tissue deformation due to respiration. [6]
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244.3M

Low quality in vivo data captured using a Medigus Camera mounted on an articulated laparoscopic robot during an intra-abdominal exploration. The sequence involves scale changes due to tissue motion. [6]
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42.7M

In vivo procedure. The data involves significant changes in the surgical environment introduced due to the presence of saline water used to clean the tissue surface. [6]
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79.2M

In vivo procedure. The sequence involves blur due to smoke combined with occlusion and tissue deformation due to tissue-tool interaction. [6]
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75.4M

In vivo porcine data collected during intra-abdominal exploration with a moving articulated laparoscope present in the field of view. The data involves changing illumination conditions. [6]
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0.46GB

0.35s

Porcine Procedure. Laparoscope is moved around viewing the abdomen wall. Deformation is minimal.  [1]
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0.23GB

15s

Porcine Procedure. Laparoscope is static and viewing a deforming liver. The liver deforms due to respiration. [1]
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3.4GB

120s

Ex Vivo Porcine Procedure. No Deformation. The Laparoscope is navigated around the abdomen viewing the liver, spleen and bowel. [1]
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1.0GB

62s

In vivo Procedure. Cardiac surface deforming with respriation and cardiac motion. [2]
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0.5GB

35s

In vivo Procedure. Cardiac surface deforming with respriation and cardiac motion. [2]
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0.2GB each

41s

In vivo Porcine Procedure. The laparoscope is navigated along the optical axis causing scale change in the image. [1]
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0.15GB each

23s

In vivo Porcine Procedure. The laparoscope is rotated around the optical axis causing a change in orientation in the image. [1]
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2.5GB each

8min

In vivo Porcine Procedure. Navigation to the Uterine Horn, transection of the horn and re-anastomosis. [1]
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0.15GB each

26s

In vivo Porcine Procedure. Static laparoscope, liver deforming due to respiration. [1]
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1.19GB

5.5min 

Ex vivo NOTES procedure. The endoscope navigates through the abdomen. [3]
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readme

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0.75GB each

1.5min

Validation dataset: Silicon heart phantom deforming with cardiac motion and associated CT scans. [4][5]
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1.25min

Validation dataset: Silicon heart phantom deforming with cardiac motion and associated CT scans. [4][5]
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50MB

4s

In vivo camera pan showing the abdomen wall. [1]
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0.36GB

3.5min

In vivo porcine. Camera motion and liver motion caused by respiration. [1]
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0.3GB

2.45min

In vivo porcine. Camera motion and liver motion caused by respiration. [1]
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0.26GB

2.45min

In vivo porcine. Camera motion and liver motion caused by respiration. [1]
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0.19GB

2.26min

In vivo porcine. General camera motion in abdomen. [1]
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0.31GB

3.45min

In vivo porcine. General camera motion in abdomen with tissue-tool interaction. [1]
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0.58GB

7.02min

In vivo porcine. General camera motion in abdomen with tissue-tool interaction. [1]
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0.18GB

1.44min

In vivo porcine. General camera motion in abdomen with some tissue deformation. [1]
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0.37GB

3.34min

In vivo porcine. General camera motion in abdomen with liver motion due to respiration. [1]
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0.11GB

1.20min

In vivo porcine. Camera panning around abdomen. [1]
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0.7GB

7.30min

In vivo porcine. General camera motion in abdomen with tissue deformation due to tool interaction. [1]
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0.23GB

2.26min

In vivo porcine. Tissue-tool interaction and diathermy. [1]
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0.11GB

1.24min

Ex vivo porcine. General camera motion in a static abdomen. [1]
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0.21GB

2.10min

Ex vivo porcine. General camera motion in a static abdomen. [1]
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0.21GB

2.10min

Ex vivo porcine. Visualisation of the ureter and kidney. [1]

References
[1] Peter Mountney, Danail Stoyanov and Guang-Zhong Yang: Three-Dimensional Tissue Deformation Recovery and Tracking: Introducing techniques based on laparoscopic or endoscopic images. IEEE Signal Processing Magazine. 2010 July. Volume: 27. Issue: 4. pp. 14-24.
[2] Danail Stoyanov, George Mylonas, Fani Deligianni, Ara Darzi, Guang-Zhong Yang: Soft-tissue Motion Tracking and Structure Estimation for Robotic Assisted MIS Procedures. Medical Image Computing and Computer Assisted Interventions (MICCAI05), vol. 2, pp. 139-146, 2005
[3] Mirna Lerotic, Adrian J. Chung, James Clark, Salman Valibeik and Guang-Zhong Yang: Dynamic View Expansion for Enhanced Navigation in Natural Orifice Transluminal Endoscopic Surgery Medical Image Computing and Computer Assisted Interventions. Medical Image Computing and Computer Assisted Interventions (MICCAI08), pp.467-475, 2008
[4] Danail Stoyanov, Marco Visentini-Scarzanella, Philip Pratt and Guang-Zhong Yang: Real-Time Stereo Reconstruction in Robotic Assisted Minimally Invasive Surgery. Medical Image Computing and Computer Assisted Interventions (MICCAI10), to appear in 2010.
[5] Philp Pratt, Danail Stoyanov, Marco Visentini-Scarzanella and Guang-Zhong Yang: Dynamic Guidance for Robotic Surgery using Image-Constrained Biomechanical Models. Medical Image Computing and Computer Assisted Interventions (MICCAI10), to appear in 2010.
[6] Stamatia Giannarou, Marco Visentini-Scarzanella, Guang-Zhong Yang, "Probabilistic Tracking of Affine-Invariant Anisotropic Regions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 99, 2012
[7] M. Ye, S. Giannarou, A. Meining, G.-Z. Yang. "Online Tracking and Retargeting with Applications to Optical Biopsy in Gastrointestinal Endoscopic Examinations". Medical Image Analysis. 2015.

The following people have been invloved in the collection of this data: Stamatia Giannarou, Danail Stoyanov, David Noonan, George Mylonas, Jim Clark, Marco Visentini-Scarzanella, Pete Mountney and Guang-Zhong Yang

If you have any questions about the datasets please contact stamatia.giannarou03 "at" imperial.ac.uk