论文标题
使用图像结构域的适应和边缘感知深度估计的单一单眼内窥镜图像的深度估计
Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation
论文作者
论文摘要
我们使用兰伯特表面翻译通过域的适应性和深度估计，使用多尺度的边缘损耗提出了一种从单一单眼内镜图像中提出的深度估计方法。我们采用了两步估计过程，包括来自未配对数据和深度估计的兰伯特表面翻译。器官表面上的质地和镜面反射降低了深度估计的准确性。我们将兰伯特表面翻译应用于内窥镜图像，以去除这些纹理和反射。然后，我们使用完全卷积网络（FCN）来估计深度。在训练FCN期间，估计图像和地面真实深度图像之间对象边缘相似性的改善对于获得更好的结果很重要。我们引入了一个量规范损耗函数，以提高深度估计的准确性。我们使用实际结肠镜图像定量评估了所提出的方法。估计的深度值与实际深度值成正比。此外，我们使用卷积神经网络将估计的深度图像应用于结肠镜图像的自动解剖位置识别。通过使用估计的深度图像，网络的识别精度从69.2％提高到了74.1％。
We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale edge loss. We employ a two-step estimation process including Lambertian surface translation from unpaired data and depth estimation. The texture and specular reflection on the surface of an organ reduce the accuracy of depth estimations. We apply Lambertian surface translation to an endoscopic image to remove these texture and reflections. Then, we estimate the depth by using a fully convolutional network (FCN). During the training of the FCN, improvement of the object edge similarity between an estimated image and a ground truth depth image is important for getting better results. We introduced a muti-scale edge loss function to improve the accuracy of depth estimation. We quantitatively evaluated the proposed method using real colonoscopic images. The estimated depth values were proportional to the real depth values. Furthermore, we applied the estimated depth images to automated anatomical location identification of colonoscopic images using a convolutional neural network. The identification accuracy of the network improved from 69.2% to 74.1% by using the estimated depth images.
