Computer aided detection of surgical retained foreign object for prevention. Issue 3 (18th February 2015)

• Record Type: Journal Article
• Authors: Hadjiiski, Lubomir; Marentis, Theodore C.; Chaudhury, Amrita R.; Rondon, Lucas; Chronis, Nikolaos; Chan, Heang‐Ping
• Is Part Of: Medical physics. Volume 42:Issue 3(2015)
• Page Start: 1213
• Page End: 1222
• Publication Date: 2015-02-18
• Subjects: diagnostic radiography -- medical image processing -- object detection; Radiography; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; computer aided detection (CAD) -- surgical retained foreign objects -- gossypiboma -- radiograph; Radiography -- Radiologists -- Image detection systems -- Artificial neural networks -- Three dimensional sensing -- Three dimensional image processing -- X‐ray detectors -- Image sensors -- Visibility; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4907964
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.