  Accurate 3D sensing of suturing thread is a challenging problem in automated
surgical suturing because of the high state-space complexity, thinness and
deformability of the thread, and possibility of occlusion by the grippers and
tissue. In this work we present a method for tracking surgical thread in 3D
which is robust to occlusions and complex thread configurations, and apply it
to autonomously perform the surgical suture "tail-shortening" task: pulling
thread through tissue until a desired "tail" length remains exposed. The method
utilizes a learned 2D surgical thread detection network to segment suturing
thread in RGB images. It then identifies the thread path in 2D and reconstructs
the thread in 3D as a NURBS spline by triangulating the detections from two
stereo cameras. Once a 3D thread model is initialized, the method tracks the
thread across subsequent frames. Experiments suggest the method achieves a 1.33
pixel average reprojection error on challenging single-frame 3D thread
reconstructions, and an 0.84 pixel average reprojection error on two tracking
sequences. On the tail-shortening task, it accomplishes a 90% success rate
across 20 trials. Supplemental materials are available at
https://sites.google.com/berkeley.edu/autolab-surgical-thread/ .
