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Computer aided detection of surgical retained foreign object for prevention
Author(s) -
Hadjiiski Lubomir,
Marentis Theodore C.,
Chaudhury Amrita R.,
Rondon Lucas,
Chronis Nikolaos,
Chan HeangPing
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4907964
Subject(s) - computer science , cad , computer vision , orientation (vector space) , workflow , artificial intelligence , set (abstract data type) , visibility , graphical user interface , radiography , interface (matter) , visualization , sensitivity (control systems) , medicine , radiology , mathematics , engineering , engineering drawing , physics , geometry , optics , bubble , database , maximum bubble pressure method , parallel computing , electronic engineering , programming language
Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 10 9 annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three‐dimensional (3D) gossypiboma micro tag, the μ Tag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μ Tag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for the surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μ Tag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μ Tag and other common man‐made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μ Tag and the remaining 739 were without μ Tag. A radiologist marked the location of the μ Tag using an in‐house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μ Tag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96.1% sensitivity with 0.81 FPs per image in a high sensitivity mode for the radiologists. On the independent test set, the CAD achieved 79.5% sensitivity with 0.003 FPs per image in a high specificity mode for the surgeons and 90.2% sensitivity with 0.23 FPs per image in a high sensitivity mode for the radiologists. Conclusions: To the best of the authors’ knowledge, this is the first time a 3D μ Tag is used to produce a recognizable, substantially similar 2D projection on radiographs regardless of orientation in space. It is the first time a CAD system is used to search for man‐made objects over anatomic background. The CAD system for the μ Tags achieved reasonable performance in both the high specificity and the high sensitivity modes.