Thesis Proposal: Yufan He
Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Retina OCT image analysis using deep learning methods
Abstract: Optical coherence topography (OCT) is a non-invasive imaging modality which uses low-coherence light waves to take cross-sectional images of optical scattering media (e.g., the human retina). OCT has been widely used in diagnosing retinal and neural diseases by imaging the human retina. The thickness of retina layers are important biomarkers for neurological diseases like multiple sclerosis (MS). The peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell plus inner plexiform layer (GCIP) thickness can be used to assess global disease progression of MS patient. Automated OCT image analysis tools are critical for quantitatively monitoring disease progression and explore biomarkers. With the development of more powerful computational resources, deep learning based methods have achieved much better performance in accuracy, speed, and algorithm flexibility for many image analysis tasks. However, these emerging deep learning methods are not satisfactory when directly applied to OCT image analysis tasks like retinal layer segmentation if not using task specific knowledge.
This thesis aims to develop a set of novel deep learning based methods for retinal OCT image analysis. Specifically, we are focusing on retinal layer segmentation from macular OCT images. Image segmentation is the process of classifying each pixel in a digital image into different classes. Deep learning methods are powerful classifiers in pixel classification, but it is hard to incorporate explicit rules. For retinal layer OCT images, pixels belonging to different layer classes must satisfy the anatomical hierarchy (topology): pixels of the upper layers should have no overlap or gap with pixels of layers beneath it. This topological criterion is usually achieved by sophisticated post-processing methods, which current deep learning method cannot guarantee. To solve this problem, we aim to:
- Develop an end-to-end deep learning segmentation method with guaranteed layer segmentation topology for retinal OCT images.
The deep learning model’s performance will degrade badly when test data is generated differently from the training data; thus, we aim to
- Develop domain adaptation methods to increase robustness of the deep learning methods to OCT images generated differently from network training data.
The deep learning pipeline will be used to analyze longitudinal OCT images for MS patients, where the subtle changes due to the MS should be captured; thus, we aim to:
- Develop a longitudinal OCT image analysis pipeline for consistent longitudinal segmentation with deep learning.