Unsupervised Task Segmentation Approach for Bimanual Surgical Tasks using Spatiotemporal and Variance Properties

Abstract

In surgical workflow analysis and training in robot-assisted surgery, automatic task segmentation could significantly reduce the manual labeling time and enhance robot learning efficiency. This paper presents an unsupervised segmentation approach to automatically segment a given surgical task without manual intervention. A new segmentation method is presented, which relies only on bimanual kinematic trajectories without the need for prior information about the data. Specifically, surgical tasks are segmented by fusing trajectories' spatiotemporal and variance properties. To demonstrate the effectiveness of the proposed method, detailed experiments were first conducted on our dataset. We segmented trajectories of three different surgical stitches and observed an average F1 score of 77.9% against the ground truths. The same trajectories were then added with different levels of noises and the segmentation comparison was made with four other methods. The proposed algorithm had demonstrated its robustness against the noises. Finally, to assess its generalization ability, the method was evaluated on publicly available JIGSAWS dataset and an average F1 score of 75.5% was achieved.