# The Neural Networks Based Needle Detection for Medical Retinal Surgery

arXiv: https://arxiv.org/abs/2302.05034 · HTML: https://mlsanity.com/paper/2302.05034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2023.

## Built on by (most cited first, 6 of 6)

- [Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data](https://mlsanity.com/paper/2501.04735/) (2025)
- [Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors](https://mlsanity.com/paper/2505.14916/) (2025)
- [End-to-end reconstruction of OCT optical properties and speckle-reduced structural intensity via physics-based learning](https://mlsanity.com/paper/2602.02721/) (2026)
- [Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications](https://mlsanity.com/paper/2602.02755/) (2026)
- [Super-Resolution and Denoising of Corneal B-Scan OCT Imaging Using Diffusion Model Plug-and-Play Priors](https://mlsanity.com/paper/2602.02795/) (2026)
- [Real-time topology-aware M-mode OCT segmentation for robotic deep anterior lamellar keratoplasty (DALK) guidance](https://mlsanity.com/paper/2602.02798/) (2026)

## Abstract (arXiv)

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.