A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics
A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics
复制标题
DOI：
10.1109/tmrb.2021.3054326
复制
发表时间：
2021-02-01
期刊：
影响因子：
--
通讯作者：
Valdastri, Pietro
中科院分区：
文献类型：
--
作者：
Attanasio, Aleks;Alberti, Chiara;Valdastri, Pietro
在手术机器人中，实现高度自主的能力往往受到手术场景复杂性的限制。与软组织的自主交互要求机器能够实时检查和理解内窥镜视频流，并识别感兴趣的特征。在这项工作中，我们展示了基于U-Net的时空神经网络的第一个例子，旨在分割内窥镜图像中的软组织。该网络配备了长短期记忆和注意门单元，可以提取内窥镜视频流中连续帧之间的相关性，从而提高了相对于标准U-Net的分割精度。首先，对三种时空层的配置进行比较，以选择最佳的结构。然后对网络参数进行优化，最后将优化结果与标准U-Net进行比较。通过实现长短期记忆（LSTM）卷积层和注意门模块，准确率分别达到83.77% +/- 2.18%和78.42% +/- 7.38%。该结果虽然起源于外科组织收缩的背景下，但可用于许多自主任务，如消融、缝合和清创。
In surgical robotics, the ability to achieve high levels of autonomy is often limited by the complexity of the surgical scene. Autonomous interaction with soft tissues requires machines able to examine and understand the endoscopic video streams in real-time and identify the features of interest. In this work, we show the first example of spatio-temporal neural networks, based on the U-Net, aimed at segmenting soft tissues in endoscopic images. The networks, equipped with Long Short-Term Memory and Attention Gate cells, can extract the correlation between consecutive frames in an endoscopic video stream, thus enhancing the segmentation's accuracy with respect to the standard U-Net. Initially, three configurations of the spatio-temporal layers are compared to select the best architecture. Afterwards, the parameters of the network are optimised and finally the results are compared with the standard U-Net. An accuracy of 83.77% +/- 2.18% and a precision of 78.42% +/- 7.38% are achieved by implementing both Long Short Term Memory (LSTM) convolutional layers and Attention Gate blocks. The results, although originated in the context of surgical tissue retraction, could benefit many autonomous tasks such as ablation, suturing and debridement.