Department of Surgery, School of Medicine, Johns Hopkins Medical Institute, Baltimore, Maryland 21224, USA.
J Surg Res. 2012 Jun 15;175(2):e47-52. doi: 10.1016/j.jss.2011.11.1018. Epub 2011 Dec 13.
More than 98% of intra-operative X-rays taken to search for postoperative retained foreign bodies (RFBs) have negative findings; in over 30% of cases of such X-rays, the finding is a false negative. Newer technologies created to find RFBs must not only reduce the false-negative rate, but also must not increase the burden of detecting RFBs. We have introduced the use of computer-aided detection (CAD) to facilitate the detection of RFBs on X-rays utilizing a modified version of map-seeking circuit (MSC) algorithm the referenced map-seeking circuit (RMSC), for our proof-of-concept study for detection of needles in plain abdominal X-rays.
Images were obtained by using a portable cassette-based X-ray machine and a C-arm (digital) machine, both of which are commonly used in the operating room. The images obtained using these machines were divided into subimages of approximately 250 × 250 pixels each, for a total of 455 subimages from the cassette-based machine (A) and 365 from the digital machine (B) for use as test samples. Images obtained from A and B were analyzed separately using our modified MSC algorithm with a minimum (τ = 0) and a maximum threshold (τ = 0.5).
The automated detection rate (positive predictive value) was 86%, with a false positive/negative rate of 10% to 15% when τ was zero.
The CAD-based RMSC algorithm has the potential to improve the accuracy with which RFBs can be found in X-rays. Further research is needed to optimize the detection rate and to identify a wider range of RFBs.
超过 98%的术中 X 射线检查是为了寻找术后残留异物（RFB），但其中 98%的检查结果为阴性；在超过 30%的此类 X 射线检查中，结果为假阴性。为了寻找 RFB，开发了一些新技术，这些技术不仅要降低假阴性率，还不能增加检测 RFB 的负担。我们引入了计算机辅助检测（CAD），以利用改进的寻图电路（MSC）算法（参考寻图电路（RMSC））来辅助检测 X 射线中的 RFB，这是我们在平腹 X 射线中检测针的概念验证研究中使用的方法。
使用便携式盒式 X 射线机和 C 臂（数字）机获取图像，这两种机器在手术室中都很常用。使用这些机器获得的图像被分为大约 250×250 像素的子图像，总共从盒式机器（A）获得 455 个子图像，从数字机器（B）获得 365 个子图像，作为测试样本。使用我们的改进的 MSC 算法分别对来自 A 和 B 的图像进行分析，最小阈值（τ=0）和最大阈值（τ=0.5）。
当 τ 为零时，自动检测率（阳性预测值）为 86%，假阳性/阴性率为 10%至 15%。
基于 CAD 的 RMSC 算法有可能提高在 X 射线中发现 RFB 的准确性。需要进一步研究以优化检测率并识别更广泛的 RFB。
