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

This study presents a CNN-based method, augmented with synthetic images, to estimate the pose of a suture needle in monocular vision. Experimental results indicate that the method effectively estimates the 2D positions and 3D orientations of the suture needle in synthetic images. The model also shows reasonable performance with real data, highlighting its promise for real-time application in robotic microsurgery.