Cross-Scene Suture Thread Parsing for Robot Assisted Anastomosis based on Joint Feature Learning

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.