ITAP (Instituto de Tecnologías Avanzadas de la Producción), University of Valladolid, School of Industrial Engineering, Paseo del Cauce, 59, 47011 Valladolid, Spain.
Comput Methods Programs Biomed. 2020 Jul;190:105378. doi: 10.1016/j.cmpb.2020.105378. Epub 2020 Feb 4.
Inadvertent retained surgical gauzes are an infrequent medical error but can have devastating consequences in the patient health and in the surgeon professional reputation. This problem seems easily preventable implementing standardized protocols for counting but due to human errors it still persists in surgery. The omnipresence of gauzes, their small size, and their similar appearance with tissues when they are soaked in blood make this error eradication really complex. In order to reduce the risk of accidental retention of surgical sponges in laparoscopy operations, in this paper we present an image processing system that tracks the gauzes on the video captured by the endoscope.
The proposed image processing application detects the presence of gauzes in the video images using texture analysis techniques. The process starts dividing the video frames into square blocks and each of these blocks is analyzed to determine whether it is similar to the gauze pattern. The video processing algorithm has been tested in a laparoscopic simulator under different conditions: with clean, slightly stained and soaked in blood gauzes as well as against different biological background tissues. Several methods, including different Local Binary Patterns (LBP) techniques and a convolutional neural network (CNN), have been analyzed in order to achieve a reliable detection in real time.
The proposed LBP algorithm classifies the individual blocks in the image with 98% precision and 94% sensitivity which is sufficient to make a robust detection of any gauze that appears in the endoscopic video even if it is stained or soaked in blood. The results provided by the CNN are superior with 100% precision and 97% sensitivity, but due to the high computational demand, real-time video processing is not attainable in this case with standard hardware.
The algorithm presented in this paper is a valuable tool to avoid the retention of surgical gauzes not only because of its reliability but also because it processes the video transparently and unattended, without the need for additional manipulation of special equipment in the operating room.
无意遗留的手术纱布是一种罕见的医疗失误，但会对患者健康和外科医生的专业声誉造成毁灭性的后果。这个问题似乎通过实施计数标准化协议就可以轻松预防，但由于人为错误，它仍然存在于手术中。纱布无处不在，它们体积小，当被血液浸湿时与组织相似，这使得消除这种错误变得非常复杂。为了降低腹腔镜手术中意外保留手术纱布的风险，本文提出了一种图像处理系统，用于跟踪内窥镜捕获的视频中的纱布。
所提出的图像处理应用程序使用纹理分析技术检测视频图像中纱布的存在。该过程首先将视频帧划分为正方形块，然后分析每个块以确定它是否与纱布模式相似。该视频处理算法已经在腹腔镜模拟器中进行了测试，包括不同的清洁程度、轻度污染和浸泡在血液中的纱布以及不同的生物背景组织。为了实现可靠的实时检测，分析了几种方法，包括不同的局部二值模式（LBP）技术和卷积神经网络（CNN）。
所提出的 LBP 算法对图像中的单个块进行分类，精度为 98%，灵敏度为 94%，足以对内镜视频中出现的任何纱布进行稳健检测，即使纱布被污染或浸泡在血液中。CNN 提供的结果更优，精度为 100%，灵敏度为 97%，但由于计算需求高，在这种情况下，标准硬件无法实现实时视频处理。
本文提出的算法是避免手术纱布遗留的有价值工具，不仅因为其可靠性，还因为它透明且无需人工干预地处理视频，无需在手术室中对特殊设备进行额外操作。
