Surgical instrument posture estimation and tracking based on LSTM

Abstract

The surgical navigation system enhances surgical safety and accuracy by providing precise guidance. However, traditional pose estimation algorithms lack real-time performance and accuracy. To address this issue, a multi-average Long Short Term Memory (LSTM) prediction network is designed to maintain sensitivity in estimating the position of surgical instruments and track their random motion trends. Additionally, the spatial coordinates of positioning markers are applied back to the imaging plane, reducing the recognition range and improving algorithm running speed. Experimental results show that the average time of estimation is less than 1ms while ensuring the prediction effect.