The Neural Networks Based Needle Detection for Medical Retinal Surgery

TL;DR

This paper presents a deep learning-based method using YOLOv5 for accurate needle detection and angle measurement in medical retinal surgery, improving upon existing segmentation approaches.

Contribution

It introduces a novel needle detection approach that incorporates angle examination and a new classification method based on needle positions.

Findings

Achieves an average Euclidean distance of 4.80 for needle tip detection.

Attains an average error of 0.85 degrees in needle angle measurement.

Demonstrates high accuracy in needle position and angle detection in surgical images.

Abstract

In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets.