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Proceedings Paper

Textual fiducial detection in breast conserving surgery for a near-real time image guidance system
Author(s): Winona L. Richey; Jon Heiselman; Ma Luo; Ingrid M. Meszoely; Michael I. Miga
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Paper Abstract

Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast cancer surgery provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent information leads to difficulty in ensuring complete tumor resection. Here we propose a novel image guidance platform that utilizes character-based fiducials for easy detection and small fiducial points for precise and accurate localization. Our system is work-flow friendly, and near-real time with use of stereo cameras for surface acquisition. Using simple image processing techniques, the proposed technique can localize fiducials and character labels, providing updates without relying on video history. Character based fiducial labels can be recognized and used to determine correspondence between left and right images in a pair of stereo cameras, and frame to frame in a sequence of images during a procedure. Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized. The stereo camera system can determine surface points with accuracy below 2mm when compared to optically tracked stylus points. These surface points are incorporated to a four-panel guidance display that includes preoperative supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151L (16 March 2020); doi: 10.1117/12.2550662
Show Author Affiliations
Winona L. Richey, Vanderbilt Univ. (United States)
Jon Heiselman, Vanderbilt Univ. (United States)
Ma Luo, Vanderbilt Univ. (United States)
Ingrid M. Meszoely, Vanderbilt Univ. Medical Ctr. (United States)
Michael I. Miga, Vanderbilt Univ. (United States)
Vanderbilt Institute for Surgery and Engineering (United States)
Vanderbilt Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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