# Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening

arXiv: https://arxiv.org/abs/2307.06845 · HTML: https://mlsanity.com/paper/2307.06845/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [Object Finding in Cluttered Scenes Using Interactive Perception](https://mlsanity.com/paper/1911.07482/) (2019)
- [Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter](https://mlsanity.com/paper/2202.02207/) (2022)
- [Deformable One-Dimensional Object Detection for Routing and Manipulation](https://mlsanity.com/paper/2201.06775/) (2022)
- [SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception](https://mlsanity.com/paper/2209.13706/) (2022)
- [HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation](https://mlsanity.com/paper/2303.08975/) (2023)
- [Suture Thread Spline Reconstruction from Endoscopic Images for Robotic Surgery with Reliability-driven Keypoint Detection](https://mlsanity.com/paper/2209.13657/) (2022)
- [All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators](https://mlsanity.com/paper/2203.04566/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)

## Abstract (arXiv)

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/ .