Multi-Stage Suture Detection for Robot Assisted Anastomosis Based on Deep Learning

Abstract

The technique of robust suture detection is vital in many applications including trainee suturing skill evaluation, suture augmentation in robotic-assisted surgery and suture recognition for automatic suturing. Due to the complicated environment of surgery, the detection of a suture is challenged by high deformation and frequent occlusion. In this paper, we propose a deep multi-stage framework for suture detection. The fully convolutional neural networks are firstly used to predict a gradient map which not only serves as a segmentation mask, but also provides useful structure information for the following thread centerline reconstruction. An overlapping map is also predicted to improve the quality of the gradient map in self-intersection area. Based on the gradient map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two types of threads demonstrate that the proposed method is able to detect the thread with human level performance when the thread is no occlusion or under finite self-intersection.