Design of the Image-Guided Biopsy Marking System for Gastroscopy

Endoscopists currently rely on an invasive biopsy tattooing method to identify previously biopsied sites. In order to better guide endoscopists to find the biopsy positions in follow-ups, we proposed a non-invasive image guided biopsy marking system for gastroscopy. Using an electromagnetic tracking device, the position of the gastroscope relative to the stomach was acquired and displayed in the guidance interface. The biopsy positions were recorded in computer for the use of guidance in follow-ups. The accuracy of the system was evaluated by both phantom experiments and in vivo experiments. The average target registration errors on the test animal and the volunteer are 13.4 mm and 11.2 mm respectively. Although the positioning error is slightly larger than current biopsy tattooing method, it satisfies the need for guidance. In the near future, we will validate the system by measuring how much it saves examination time.

[1]  William A. Barrett,et al.  Interactive live-wire boundary extraction , 1997, Medical Image Anal..

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Luc Soler,et al.  An augmented reality system to guide radio‐frequency tumour ablation , 2005, Comput. Animat. Virtual Worlds.

[4]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[5]  Bernhard Preim,et al.  Intraoperative augmented reality for minimally invasive liver interventions , 2003, SPIE Medical Imaging.

[6]  Joo Young Cho,et al.  Usefulness of three-dimensional, multidetector row CT (virtual gastroscopy and multiplanar reconstruction) in the evaluation of gastric cancer: a comparison with conventional endoscopy, EUS, and histopathology. , 2004, Gastrointestinal endoscopy.

[7]  Hyo Won Eun,et al.  Imaging of various gastric lesions with 2D MPR and CT gastrography performed with multidetector CT. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[8]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[9]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[10]  K. Cleary,et al.  Image-Guided Procedures : A Review , 2006 .

[11]  Mark Hastenteufel,et al.  Navigation aids and real-time deformation modeling for open liver surgery , 2003, SPIE Medical Imaging.

[12]  Zhang Hong To Prove Burse-Wolf Conversion Model with Simple Formula , 2006 .

[13]  Jan J. Gerbrands,et al.  Three-dimensional image segmentation using a split, merge and group approach , 1991, Pattern Recognit. Lett..

[14]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[15]  M. Canto,et al.  Staining in Gastrointestinal Endoscopy: The Basics , 1999, Endoscopy.

[16]  Y. Fukui,et al.  Real-time 3-D model-based navigation system for endoscopic paranasal sinus surgery , 1999, IEEE Transactions on Biomedical Engineering.

[17]  J. J. Gerbrands Segmentation of noisy images , 1988 .

[18]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[19]  Yu-jing Fan,et al.  Trial of a novel endoscopic tattooing biopsy forceps on animal model. , 2005, World journal of gastroenterology.

[20]  Aytekin Oto,et al.  Virtual endoscopy. , 2002, European journal of radiology.

[21]  Jan J. Gerbrands,et al.  Thresholding three-dimensional image , 1990, Other Conferences.

[22]  Luc Soler,et al.  A Complete Augmented Reality Guidance System for Liver Punctures: First Clinical Evaluation , 2005, MICCAI.

[23]  William E. Lorensen,et al.  The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics , 1998 .