Cross-Scene Suture Thread Parsing for Robot Assisted Anastomosis based on Joint Feature Learning
Cross-Scene Suture Thread Parsing for Robot Assisted Anastomosis based on Joint Feature Learning
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DOI：
10.1109/iros.2018.8593622
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yun Gu;Yang Hu;Lin Zhang;J. Yang;Guang-Zhong Yang
中科院分区：
文献类型：
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作者：
Yun Gu;Yang Hu;Lin Zhang;J. Yang;Guang-Zhong Yang
任务自主性是未来手术机器人发展的重要考虑因素。对于机器人辅助吻合，缝线检测是后续机器人操作的先决条件。先前关于自动螺纹检测的工作集中在具有特定手术设置的模型的学习上，这些手术设置难以推广到通用设置。在本文中，我们提出了一个联合特征学习框架，以满足手术缝合线检测的前景和背景适应。所提出的方法是在半监督和无监督域自适应的背景下开发的，利用来自源域的标记训练数据来学习未标记或部分标记的目标域的检测模型，这些目标域也可以来自不同类型的线程或器官。基于对抗学习，我们进一步保留语义身份，并引入课程适应来生成合成数据。四个领域的适应任务缝合线检测的实验表明，所提出的方法能够生成高质量的合成数据和特定域之间的传输有限，甚至没有标记的数据的目标域的强度。
Task autonomy is an important consideration for the development of future surgical robots. For robot-assisted anastomosis, suture thread detection is a prerequisite for subsequent robot manipulation. Previous works on automatic thread detection are focused on the learning of the models with specific surgical settings that are poorly generalisable to generic settings. In this paper, we propose a joint feature learning framework that caters for the foreground and background adaptation for surgical suture thread detection. The proposed method is developed in the context of semi-supervised and unsupervised domain adaptation, leveraging the labelled training data from the source domain to learn the detection model for unlabelled or partially labelled target domain, which can also be from different types of threads or organs. Based on adversarial learning, we further preserve the semantic identity and introduce curriculum adaptation to generate synthetic data. Experiments on four domain adaptation tasks for suture thread detection demonstrate the strength of the proposed method being able to generate good quality synthetic data and transfer between specific domains with limited or even no labelled data of the target domain.