论文标题
使用嵌入式暗通道引导的图像到图像翻译的腹腔镜手术图像
Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
论文作者
论文摘要
在腹腔镜手术中，图像的可见性可能会因$ co_2 $注射引起的烟雾和解剖工具而严重降解，从而降低了器官和组织的可见性。缺乏可见性会增加手术时间，甚至增加外科医生造成的错误的可能性，然后对患者的健康产生负面影响。在本文中，引入了一种消除烟雾效应的新型计算方法。所提出的方法基于图像到图像的条件生成对抗网络，其中暗通道用作嵌入式导膜。评估获得的实验结果，并使用峰信噪比（PSNR）和结构相似性（SSIM）指数进行定量与其他浸入和脱掩护的最新方法进行比较。基于这些指标，发现所提出的方法与最先进的方法相比提高了性能。此外，我们方法所需的处理时间为每秒92帧，因此可以在实时医疗系统槽中应用嵌入式设备。
In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.
