Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning

TL;DR

This paper introduces a multi-stage deep learning framework for reliable suture thread detection in robotic surgery, addressing challenges like occlusion and complex topologies to improve automation of anastomosis tasks.

Contribution

The authors develop a novel multi-stage deep learning approach combining CNNs and curvilinear structure detection for accurate suture detection in complex surgical environments.

Findings

High accuracy in suture detection demonstrated on two suture types.

Effective handling of occlusion and complex thread topologies.

Framework suitable for automation in robotic surgical procedures.

Abstract

In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms. One such task is automated anastomosis, which requires bimanual needle handling and suture detection. Due to the complexity of the surgical environment and varying patient anatomies, reliable suture detection is difficult, which is further complicated by occlusion and thread topologies. In this paper, we propose a multi-stage framework for suture thread detection based on deep learning. Fully convolutional neural networks are used to obtain the initial detection and the overlapping status of suture thread, which are later fused with the original image to learn a gradient road map of the thread. Based on the gradient road map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two different types of sutures demonstrate the accuracy of the proposed framework.