Monocular suture needle pose detection using synthetic data augmented convolutional neural network.

ABSTRACT:

Purpose

Robotic microsurgery enhances the dexterity and stability of the surgeon to perform precise and delicate surgical procedures at microscopic level. Accurate needle pose estimation is critical for robotic micro-suturing, enabling optimized insertion trajectories and facilitating autonomous control. However, accurately estimating the pose of a needle during manipulation, particularly under monocular vision, remains a challenge. This study proposes a convolutional neural network-based method to estimate the pose of a suture needle from monocular images.

Methods

The 3D pose of the needle is estimated using keypoints information from 2D images. A convolutional neural network was trained to estimate the positions of keypoints on the needle, specifically the tip, middle and

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12518471 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature