Depth Estimation for Instrument Segmentation from a Single Laparoscopic Video toward Laparoscopic Surgery Support
Depth Estimation for Instrument Segmentation from a Single Laparoscopic Video toward Laparoscopic Surgery Support
复制标题
从单个腹腔镜视频到腹腔镜手术支持的器械分割深度估计
DOI：
10.1145/3332340.3332347
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Y. Mekada
中科院分区：
文献类型：
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作者：
Takuya Suzuki;Keisuke Doman;Y. Mekada
为了提高使用手术支持系统的腹腔镜手术的安全性，有必要从腹腔镜图像中提取手术器械，如钳子。对于手术器械的图像分割，深度学习技术如全卷积神经网络(FCN)是有效的。众所周知，使用立体摄像机可以提高分割精度，因为手术器械上的深度信息和颜色信息应该是有用的。本文提出了一种基于FCN的单目摄像机采集的单幅腹腔镜图像的深度估计方法。提出了一种利用估计的深度信息和颜色信息的U网图像分割方法。与仅使用颜色信息的比较方法相比，在使用MICAI挑战的数据集上，我们的方法将平均IOU和Dice系数都提高了约2%。我们证实了我们方法的有效性。
It is necessary to extract surgical instruments such as forceps from laparoscopic images in order to improve the safety of laparoscopic surgery using a surgery support system. For image segmentation for surgical instruments, a deep learning technique such as a fully-convolutional neural network (FCN) is effective. It is known that the segmentation accuracy can be improved by using a stereo camera, because the depth information as well as color information on surgical instruments should be useful. This paper proposes a FCN-based depth estimation method from a single laparoscopic image captured by a monocular camera. And also proposes a U-Net-based image segmentation method using on the estimated depth information as well as color information. In experiments with the dataset of the MICCAI challenge, our method improved both the average IOU and Dice coefficient by about 2%, comparing with a comparative method using only color information. We confirmed the effectiveness of our method.