Deep learning methods for real-time corneal and needle segmentation in volumetric OCT scans
Agrawal, Harsh
Loading…
Permalink
https://hdl.handle.net/2142/110630
Description
Title
Deep learning methods for real-time corneal and needle segmentation in volumetric OCT scans
Author(s)
Agrawal, Harsh
Issue Date
2021-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Hauser, Kris
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
deep learning
machine learning
image segmentation
OCT
cornea segmentation
tool tracking
Abstract
Deep lamellar anterior keratoplasty (DALK) is a promising cornea transplant procedure, which mitigates the risks associated with the commonly used alternative penetrating keratoplasty. In DALK, a surgeon must insert a needle into the cornea to a precise depth and inject an air bubble to separate the stroma and endothelial layer. Optical coherence tomography (OCT) is used to help surgeons judge the needle insertion depth. However, the needle obscures the part of the cornea underneath it, making it difficult to estimate needle insertion depth. In this thesis, deep learning methods are explored for cornea and needle segmentation to automatically compute needle insertion depth from volumetric OCT scans. A simple post-processing step is applied to the mask produced by the deep neural networks to determine the coordinates of the top and bottom corneal boundaries and the needle tip. We compare the performance of 2D and 3D networks on this task. Our experiments consistently showed that all networks perform well on cornea segmentation in scans without a needle, and 3D networks perform better in scans with a needle. We also show that these deep learning methods perform better than existing graph-theory methods on this task and can segment in real-time when deployed using the NVIDIA TensorRT framework.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
