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Proceedings Paper

Surgical retained foreign object (RFO) prevention by computer aided detection (CAD)
Author(s): Theodore C. Marentis; Lubomir Hadjiiyski; Amrita R. Chaudhury; Lucas Rondon; Nikolaos Chronis; Heang-Ping Chan
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Paper Abstract

Surgical Retained Foreign Objects (RFOs) cause significant morbidity and mortality. They are associated with $1.5 billion annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. We address the RFO problem with two complementary technologies: a three dimensional (3D) Gossypiboma Micro Tag (μTa) that improves the visibility of RFOs on radiographs, and a Computer Aided Detection (CAD) system that detects the μTag. The 3D geometry of the μTag produces a similar 2D depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. We create a database of cadaveric radiographs with the μTag and other common man-made objects positioned randomly. We develop the CAD modules that include preprocessing, μTag enhancement, labeling, segmentation, feature analysis, classification and detection. The CAD can operate in a high specificity mode for the surgeon to allow for seamless workflow integration and function as a first reader. The CAD can also operate in a high sensitivity mode for the radiologist to ensure accurate detection. On a data set of 346 cadaveric radiographs, the CAD system performed at a high specificity (85.5% sensitivity, 0.02 FPs/image) for the OR and a high sensitivity (96% sensitivity, 0.73 FPs/image) for the radiologists.

Paper Details

Date Published: 18 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903529 (18 March 2014); doi: 10.1117/12.2042469
Show Author Affiliations
Theodore C. Marentis, Univ. of Michigan (United States)
Lubomir Hadjiiyski, Univ. of Michigan (United States)
Amrita R. Chaudhury, Univ. of Michigan (United States)
Lucas Rondon, Univ. of Michigan (United States)
Nikolaos Chronis, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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