EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms

Background: 3D reconstruction algorithms are of fundamental importance for augmented reality applications in computer‐assisted surgery. However, few datasets of endoscopic stereo images with associated 3D surface references are currently openly available, preventing the proper validation of such algorithms. This work presents a new and rich dataset of endoscopic stereo images (EndoAbS dataset).

[1]  Luc Soler,et al.  The status of augmented reality in laparoscopic surgery as of 2016 , 2017, Medical Image Anal..

[2]  Elena De Momi,et al.  Development of a surgical stereo endoscopic image dataset for validating 3D stereo reconstruction algorithms , 2016 .

[3]  J. Marescaux,et al.  Augmented reality in laparoscopic surgical oncology. , 2011, Surgical oncology.

[4]  Harald Essig,et al.  Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts , 2015, International Journal of Computer Assisted Radiology and Surgery.

[5]  Wen-Chun Yeh,et al.  Elastic modulus measurements of human liver and correlation with pathology. , 2002, Ultrasound in medicine & biology.

[6]  Lena Maier-Hein,et al.  Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery , 2013, Medical Image Anal..

[7]  V Ferrari,et al.  How to build patient‐specific synthetic abdominal anatomies. An innovative approach from physical toward hybrid surgical simulators , 2011, The international journal of medical robotics + computer assisted surgery : MRCAS.

[8]  Sebastian Bodenstedt,et al.  Dense GPU-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration. , 2012, Medical physics.

[9]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[10]  Guang-Zhong Yang,et al.  Dynamic Guidance for Robotic Surgery Using Image-Constrained Biomechanical Models , 2010, MICCAI.

[11]  Guang-Zhong Yang,et al.  Motion Compensated SLAM for Image Guided Surgery , 2010, MICCAI.

[12]  Guang-Zhong Yang,et al.  Real-Time Stereo Reconstruction in Robotically Assisted Minimally Invasive Surgery , 2010, MICCAI.

[13]  Lena Maier-Hein,et al.  Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction , 2014, IEEE Transactions on Medical Imaging.

[14]  Elena De Momi,et al.  Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery , 2016, International Journal of Computer Assisted Radiology and Surgery.

[15]  Pierre Jannin,et al.  Model for defining and reporting reference-based validation protocols in medical image processing , 2006, International Journal of Computer Assisted Radiology and Surgery.

[16]  Wen P. Liu.  Dynamic Guidance for Robotic Surgery Using Image-Constrained Biomechanical Model , 2011 .

[17]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[18]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Danail Stoyanov,et al.  Surgical Vision , 2011, Annals of Biomedical Engineering.

[20]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Naoki Suzuki,et al.  Clinical application of navigation surgery using augmented reality in the abdominal field , 2015, Surgery Today.

[22]  E G Shifrin,et al.  Laparoscopic assisted aortic surgery. A review. , 2006, The Journal of cardiovascular surgery.

[23]  Leonardo S. Mattos,et al.  Virtual Assistive System for Robotic Single IncisionLaparoscopic Surgery , 2014 .

[24]  Xi Wang,et al.  High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth , 2014, GCPR.

[25]  A. Ahluwalia,et al.  Viscoelastic characterisation of pig liver in unconfined compression. , 2014, Journal of biomechanics.

[26]  Danail Stoyanov,et al.  Tissue Surface Reconstruction Aided by Local Normal Information Using a Self-calibrated Endoscopic Structured Light System , 2015, MICCAI.

[27]  A Tirella,et al.  Strain rate viscoelastic analysis of soft and highly hydrated biomaterials , 2013, Journal of biomedical materials research. Part A.

[28]  Michael Figl,et al.  3D Reconstruction of Internal Organ Surfaces for Minimal Invasive Surgery , 2007, MICCAI.

[29]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[30]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.