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A Comprehensive Framework for Adaptive Optics Scanning Light Ophthalmoscope Image Analysis

dc.contributor.advisor Farsiu, Sina
dc.contributor.author Cunefare, David
dc.date.accessioned 2020-01-27T16:52:29Z
dc.date.available 2020-09-12T08:17:08Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10161/19855
dc.description Dissertation
dc.description.abstract <p>Diagnosis, prognosis, and treatment of many ocular and neurodegenerative diseases, including achromatopsia (ACHM), require the visualization of microscopic structures in the eye. The development of adaptive optics ophthalmic imaging systems has made high resolution visualization of ocular microstructures possible. These systems include the confocal and split detector adaptive optics scanning light ophthalmoscope (AOSLO), which can visualize human cone and rod photoreceptors in vivo. However, the avalanche of data generated by such imaging systems is often too large, costly, and time consuming to be evaluated manually, making automation necessary. The few currently available automated cone photoreceptor identification methods are unable to reliably identify rods and cones in low-quality images of diseased eyes, which are common in clinical practice.</p><p>This dissertation describes the development of automated methods for the analysis of AOSLO images, specifically focusing on cone and rod photoreceptors which are the most commonly studied biomarker using these systems. A traditional image processing approach, which requires little training data and takes advantage of intuitive image features, is presented for detecting cone photoreceptors in split detector AOSLO images. The focus is then shifted to deep learning using convolutional neural networks (CNNs), which have been shown in other image processing tasks to be more adaptable and produce better results than classical image processing approaches, at the cost of requiring more training data and acting as a “black box”. A CNN based method for detecting cones is presented and validated against state-of-the-art cone detections methods for confocal and split detector images. The CNN based method is then modified to take advantage of multimodal AOSLO information in order to detect cones in images of subjects with ACHM. Finally, a significantly faster CNN based approach is developed for the classification and detection of cones and rods, and is validated on images from both healthy and pathological subjects. Additionally, several image processing and analysis works on optical coherence tomography images that were carried out during the completion of this dissertation are presented.</p><p>The completion of this dissertation led to fast and accurate image analysis tools for the quantification of biomarkers in AOSLO images pertinent to an array of retinal diseases, lessening the reliance on subjective and time-consuming manual analysis. For the first time, automatic methods have comparable accuracy to humans for quantifying photoreceptors in diseased eyes. This is an important step in the long-term goal to facilitate early diagnosis, accurate prognosis, and personalized treatment of ocular and neurodegenerative diseases through optimal visualization and quantification of microscopic structures in the eye.</p>
dc.subject Medical imaging
dc.subject Biomedical engineering
dc.subject Ophthalmology
dc.subject adaptive optics
dc.subject deep learning
dc.subject image analysis
dc.subject machine learning
dc.subject ophthalmic imaging
dc.title A Comprehensive Framework for Adaptive Optics Scanning Light Ophthalmoscope Image Analysis
dc.type Dissertation
dc.department Biomedical Engineering
duke.embargo.months 7.4958904109589035


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