Semi-supervised spatio-temporal CNN for recognition of surgical workflow

doi:10.1186/s13640-018-0316-4

	Semi-supervised spatio-temporal CNN for recognition of surgical workflow
	Chen,Yuwen1,2,3 ; Sun,Qi Long 3; Zhong,Kunhua1,2,3
刊名	EURASIP Journal on Image and Video Processing
	2018-08-25
卷号	2018 期号:1
关键词	Semi-supervised Surgical workflow CNN
ISSN号	1687-5281
DOI	10.1186/s13640-018-0316-4
英文摘要	AbstractRobust and automated surgical workflow detection in real time is a core component of the future intelligent operating room. Based on this technology, it can help medical staff to automate and intelligently complete many routine activities during surgery. Recognition of surgical workflow based on traditional pattern recognition methods requires a large number of labeled surgical video data. However, the labeled surgical video data requires expert knowledge and it is difficult and time consuming to collect a sufficient number of labeled surgical video data in the medical field. Therefore, this paper proposes a semi-supervised spatio-temporal convolutional network for the recognition of surgical workflow based on convolutional neural networks and temporal-recursive networks. Firstly, we build a spatial convolutional extraction feature network based on unsupervised generative adversarial learning. Then, we build a bridge between low-level surgical video features and high-level surgical workflow semantics based on an unsupervised temporal-ordered network learning approach. Finally, we use the semi-supervised learning method to integrate the spatial model and the temporal model to fine-tune the network, and realize the intelligent recognition of the surgical workflow at a low cost to efficiently determine the progress of the surgical workflow. We performed some experiments for validating the mode based on m2cai16-workflow dataset. It shows that the proposed model can effectively extract the surgical feature and determine the surgical workflow. The Jaccard score of the model reaches 71.3%, and the accuracy of the model reaches 85.8%.
语种	英语
出版者	Springer International Publishing
WOS记录号	BMC:10.1186/S13640-018-0316-4
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/7159]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Zhong,Kunhua
作者单位	1. 2. 3.
推荐引用方式 GB/T 7714	Chen,Yuwen,Sun,Qi Long,Zhong,Kunhua. Semi-supervised spatio-temporal CNN for recognition of surgical workflow[J]. EURASIP Journal on Image and Video Processing,2018,2018(1).
APA	Chen,Yuwen,Sun,Qi Long,&Zhong,Kunhua.(2018).Semi-supervised spatio-temporal CNN for recognition of surgical workflow.EURASIP Journal on Image and Video Processing,2018(1).
MLA	Chen,Yuwen,et al."Semi-supervised spatio-temporal CNN for recognition of surgical workflow".EURASIP Journal on Image and Video Processing 2018.1(2018).