Semi-Automated Extraction of Lens Fragments Via a Surgical Robot Using Semantic Segmentation of OCT Images with Deep Learning-Experimental Results in Ex Vivo Animal Model
Journal
IEEE Robotics and Automation Letters
Journal Volume
6
Journal Issue
3
Pages
5261-5268
Date Issued
2021
Author(s)
Abstract
The overarching goal of this letter is to demonstrate the feasibility of using optical coherence tomography (OCT) to guide a robotic system to extract lens fragments from ex vivo pig eyes. A convolutional neural network (CNN) was developed to semantically segment four intraocular structures (lens material, capsule, cornea, and iris) from OCT images. The neural network was trained on images from ten pig eyes, validated on images from eight different eyes, and tested on images from another ten eyes. This segmentation algorithm was incorporated into the Intraocular Robotic Interventional Surgical System (IRISS) to realize semi-automated detection and extraction of lens material. To demonstrate the system, the semi-automated detection and extraction task was performed on seven separate ex vivo pig eyes. The developed neural network exhibited 78.20% for the validation set and 83.89% for the test set in mean intersection over union metrics. Successful implementation and efficacy of the developed method were confirmed by comparing the preoperative and postoperative OCT volume scans from the seven experiments. ? 2016 IEEE.
Subjects
Cataract surgery
computer vision for medical robotics
deep learning
medical robots and systems
surgical robotics: planning
Agricultural robots
Convolutional neural networks
Deep learning
Extraction
Lenses
Mammals
Optical tomography
Robotic surgery
Robotics
Robots
Semantics
Surgery
Automated extraction
Interventional
Lens materials
Robotic systems
Segmentation algorithms
Semantic segmentation
Semi-automated detection
Surgical systems
Image segmentation
Type
journal article