Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset

The automated recognition of surgical instruments in surgical videos is an essential factor for the evaluation and analysis of surgery. The analysis of surgical instrument localization information can help in analyses related to surgical evaluation and decision making during surgery. To solve the problem of the localization of surgical instruments, we used an object detector with bounding box labels to train the localization of the surgical tools shown in a surgical video. In this study, we propose a semi-supervised learning-based training method to solve the class imbalance between surgical instruments, which makes it challenging to train the detectors of the surgical instruments. First, we labeled gastrectomy videos for gastric cancer performed in 24 cases of robotic surgery to detect the initial bounding box of the surgical instruments. Next, a trained instrument detector was used to discern the unlabeled videos, and new labels were added to the tools causing class imbalance based on the previously acquired statistics of the labeled videos. We also performed object tracking-based label generation in the spatio-temporal domain to obtain accurate label information from the unlabeled videos in an automated manner. We were able to generate dense labels for the surgical instruments lacking labels through bidirectional object tracking using a single object tracker; thus, we achieved improved instrument detection in a fully or semi-automated manner.