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

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 ma...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-07, Vol.6 (3), p.5261-5268
Main Authors: Shin, Changyeob, Gerber, Matthew, Lee, Yu-Hsiu, Rodriguez, Mercedes, Aghajani Pedram, Sahba, Hubschman, Jean-Pierre, Tsao, Tsu-Chin, Rosen, Jacob
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Automation
Cataract surgery
Cataracts
computer vision for medical robotics
Cornea
Deep learning
Eye (anatomy)
Fragments
Image segmentation
Iris
Lenses
medical robots and systems
Neural networks
Optical Coherence Tomography
Robots
Semantic segmentation
Surgery
surgical robotics: planning
Visualization
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3072574